Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022

Visualizing real-world features along with digital textual data and virtual objects has made augmented reality (AR) technology attractive for commercial uses such as sales, manufacturing, and construction. AR application developers are designing new applications for a variety of purposes. New AR applications related to construction are becoming increasingly available to industry practitioners. It can be challenging for them to select a truly useful one from many existing AR applications through a trial-and-error approach that is time-consuming and costly. Therefore, a systematic evaluation procedure is desired to allow users to evaluate AR applications during their trial period without having to commit significant financial resources. This study aims to develop a framework that evaluates the usability of off-the-shelf AR applications. The authors proposed a framework for assessing the usability of construction-focused augmented reality applications based on the International Organization for Standardization 9241-11:2018 standard and other relevant works of literature. Using the proposed criteria, the authors examined the five most popular AR construction apps with the highest number of downloads and at least four-star reviews to validate the proposed framework. To validate the study, participants were recruited to evaluate applications and rank them in predefined categories, including effectiveness, efficiency, and satisfaction, using the proposed framework. Out of five applications tested, two applications, VisualLive and Gamma AR, were ranked as the first and second-best applications. The ranking is consistent with the expert review of the applications. The proposed framework helps in the selection of the most useful applications from a pool of off-the-shelf software. It enables users in the construction industry to make an informed decision about AR application selection before committing time and money to acquisition and implementation.

Augmented reality (AR) and mixed reality (MR) technologies have gained significant interest throughout the past two decades in the Architecture, Engineering, and Construction (AEC) Industry. However, despite the rapid growth of these technologies, their effective implementation in the AEC industry is still in its infancy. Therefore, a comprehensive investigation of the state-of-the-art applications and categories of AR/MR in the construction industry can guide researchers and industry experts to choose the most suitable AR/MR solution for research and implementation. This paper provides a comprehensive overview of 103 AR/MR articles published in credible journals in the field of the AEC industry within the years 2013–2021. Typically, review-type papers assess articles primarily based on their application areas. However, this classification approach overlooks some other critical dimensions, such as the article’s technology type, the maturity level of technology used in the research, and the project phase in which technology is implemented. Accordingly, this paper classifies articles based on ten dimensions and their relevant categories: research methodology, improvement focus, industry sector, target audience, project phase, stage of technology maturity, application area, comparison role, technology type, and location. The results reveal that AR/MR literature has increasingly focused on simulation/visualization applications during construction and maintenance/operation phases of the project, emphasizing improving the performance of workers/technicians. Additionally, the increasing trend in AR/MR articles was identified as using self-contained headsets (e.g., Microsoft HoloLens). Markerless tracking systems show a significant trend among the articles. Moreover, the target location of implementing AR/MR primarily found to be in on-site and in outdoor spaces. The trend indicates an increase in immersive and mobile AR/MR applications in outdoor job sites such as construction sites to aid workers/technicians in assembly works during the construction phase.

Autonomous mobile robots, including unmanned aerial vehicles (UAVs), have received significant attention for their applications in construction. These platforms have great potential to automate and enhance the quality and frequency of the required data for many tasks such as construction schedule updating, inspections, and monitoring. Robust localization is a critical enabler for reliable deployments of autonomous robotic platforms. Automated robotic solutions rely mainly on the Global Positioning System (GPS) for outdoor localization. However, GPS signals are denied indoors, and pre-built environment maps are often used for indoor localization. This entails generating high-quality maps by teleoperating the mobile robot in the environment. Not only is this approach time-consuming and tedious, but it also is unreliable in indoor construction settings. Layout changes with construction progress, requiring frequent mapping sessions to support autonomous missions. Moreover, the effectiveness of vision-based solutions relying on visual features is highly impacted in low texture and repetitive areas on site. To address these challenges, we previously proposed a low-cost, lightweight tag-based visual-inertial localization method using AprilTags. Tags, in this method, are paper printable landmarks with known sizes and locations, representing the environment’s quasi-map. Since tag placement/replacement is a manual process, it is subjected to human errors. In this work, we study the impact of human error in the manual tag installation process and propose a stochastic approach to account for this uncertainty using the Lie group theory. Employing Monte Carlo simulation, we experimentally show that the proposed stochastic model incorporated in our on-manifold formulation improves the robustness and accuracy of tag-based localization against inevitable imperfections in manual tag installation on site.

The construction industry faces challenges of skilled labor shortage, low productivity, continuous cost and schedule overruns, and unsafe working conditions for workers. The application of construction robots is a potential solution to alleviate these problems by providing higher productivity, quality, and a safer working environment. Existing construction robotic solutions, such as bricklaying robots and grid drawing robots, are typically programmed to follow specific sequences of instructions designed beforehand. Recent advancements in robot control algorithms like Reinforcement Learning (RL) have enabled robots to adapt to unseen scenarios by learning sequences of optimal actions from videos of expert demonstrations. However, these demonstrations need to be collected on real construction sites, which can be costly and potentially dangerous to experts, especially when multiple demonstrations are required. In this study, we propose a novel approach that leverages Virtual Reality (VR) to collect expert demonstrations, allowing the demonstrator to illustrate the procedure of a construction task using handheld VR controllers. During demonstrations, direct parameters including the robotic arm’s joint states, positions, and orientations of the object to be manipulated are extracted from the virtual environment to generate a control policy that imitates the behavior of the expert. We implemented the proposed approach for the task of window installation as validation. The control policy was learned and later applied to a robot arm in a virtual environment. Results show that for all 10 testing cases, the control policy could successfully generate actions given the observed states and lead the robotic arm to first pick up the window and then install it at the target location. These results confirm the effectiveness of the proposed approach in providing virtual demonstrations for the robot to learn a control policy, which eliminates the need for on-site demonstrations from experts, avoiding potentially unsafe scenarios.

Statistics reveal that construction has one of the highest fatality rates among all industries. A solution to improve construction sites’ safety and avoid accidents is the precise localization of personnel and equipment. Tracking with Ultra-Wideband (UWB) has proved to be suitable for construction site applications due to its high location estimation accuracy, high signal penetration, and low power consumption. However, if the line-of-sight (LOS) between the tag and the anchors is blocked, the location estimation accuracy deteriorates. Due to the dynamic nature of the construction sites, the workers and personnel can easily block the LOS and create non-line-of-sight (NLOS) or quasi-line-of-sight (quasi-NLOS) conditions, which can lead to high errors in positioning accuracy. However, it should be determined how significantly NLOS and quasi-NLOS conditions affect the accuracy. Accordingly, the objective of this study is to investigate the effect of human body blockage on location estimation accuracy by conducting some experiments. The impact of the number of the human blocks and their position with respect to the anchors and the tag is evaluated in NLOS and quasi-NLOS scenarios. 2D and 3D accuracies are calculated to compare the estimated locations and the actual ones. The results indicated that placing the anchors at an elevation higher than the height of human blocks and keeping the human blocks closer to the anchors in comparison with the tag provides more accurate location estimations.

Occurrences of interruptions of deterministic or probabilistic nature are inevitable on typical construction projects because of unavailability of resources or human and management factors, thereby causing high variability in activity duration and lower labor productivity. This research proposes enhanced activity-on-node network diagramming method (named AON+) for construction planning and scheduling applications to formulate production schedules for repetitive workflows featuring non-uniform work units. In contrast to established project planning techniques, AON+ enables construction managers to represent details in workflows in a streamlined network diagram by sufficiently factoring in logical constraints imposed by both technology and resource. Further, an “AON+” network model readily feeds as the structured problem definition to rapidly generate discrete event simulation models for resource-constrained scheduling and operations simulation analyses. Two application cases based on industry practice are presented, namely a “rebar installation on a bridge deck” problem and a “bored pile concrete pouring” problem.

Periodical construction progress reports are essential in project evaluation and review. They impact stockholder communication, transparency, and trust. While conventional pictures and videos are currently the norm in supporting progress reporting, their use is not always efficient. Therefore, commercial products are now available for integrating 360-degree photography into progress reports. However, there is a shortage of academic studies that actually assess the effectiveness of such tools. The goal of this research is to analyze the methods, benefits, and limitations of using smart 360-degree models and QR codes in progress reporting, and how such use impacts the overall project performance. The keyword “smart” indicates that the 360-degree pictures would show additional info on them such as comments, side-by-side comparisons to previous months, all into a single 360-degree advanced progress model. To this end, information was collected from construction projects and firms to determine the status and used methods of progress reporting. Then, a pilot 3-month study was conducted where smart 360-degree pictures and QR codes are introduced as means of progress reporting for ongoing residential projects and institutional and commercial building. After thorough analysis of minutes of meetings, letters, and interviews before, during, and after using the technology, the results indicate that the choice of the integration platform is important. For example, it must have capabilities of integrating and stitching several 360-degree pictures together into one large model, linking the model to BIM and Procore software, enabling side-by-side comparison, and having a strong back-end enabling multiple users to access it remotely. The results also indicate that the proper use of such technology enhanced the overall coordination, transparency, trust, and responsibility between the project parties. It also led to less finger-pointing. Additional studies in longer periods and a larger number of projects are needed to determine the effect of such technology on cost and time.

The University of Victoria is in the process of expanding its engineering and computer science department to meet the growing demand for post-graduate programs by building two new buildings. UVic’s Green Civil Engineering department is actively involved in the project and planning to use the buildings as experimental apparatuses for various building science and systems research such as energy, water, and indoor environmental quality. These buildings aspire to achieve net zero carbon certifications to promote innovations in sustainability. Post-occupancy evaluations (POE) provide scientific methods and tools to analyze how buildings function and to quantify their performance. First, this paper establishes the semantics of POE in the context of the new engineering expansion project along with project phases. Second, this paper discusses the digital twin execution plan that can guide the evolution of digital twins during each phase of the project life cycle for the purpose of POE. Third, this paper compares the proposed digital twin-based POE methodology with the conventional POE methodology. Conducting the POE on the UVic ECS expansion project will enable the researchers to determine the effectiveness of sustainable features by comparing the performance of existing and proposed facilities.

Bridges are a vital component of the public transit system. However, as such infrastructure systems age, they sustain various sorts of damage, decreasing their performance and service life dramatically. In this setting, effective and efficient bridge health monitoring is critical to lowering maintenance costs and extending the service life of existing bridges. Traditional monitoring techniques require sensors being installed on bridges, which is costly and time-consuming. This paper presents a novel crowdsensing-based methodology to monitor the health condition of bridges through a number of smartphones in moving vehicles, i.e., indirect monitoring. By collecting continuous data from the smartphone users and extracting features from the data while they cross the bridge, the damage can be identified through quantifying the difference of the distributions of the features. The continuous data collection and transmission with high sampling frequency pose a particular challenge to the participation of the public, because this could drain the smartphone battery and data plan quickly. In this paper, compressed sensing is introduced into this crowdsensing framework. The compressed sensing can recover the signal from much fewer samples than the ones required by Nyquist–Shannon sampling theorem through random sampling, which leads to more efficient data collection and transmission. Numerical analysis is conducted to validate the effectiveness of compressed sensing on indirect bridge condition monitoring.

The ISO 19650 framework for information management in the built asset industry has formalized the notion of common data environments (CDE) on a global scale. Yet, as with any new concepts and their operationalization, there remains a lot of work to be done to adopt, implement, and deploy the practices, processes, tools, and technologies underlying the CDE at both the project and asset level. The work presented in this paper discusses the results of a survey undertaken in the Province of Quebec to understand the rate of CDE adoption and implementation, the barriers and challenges, and the current trends in the province. The paper also regroups and formalizes the concept of CDE based on a broad review of the literature to understand and better frame the functions and the practices supported through CDEs. Unsurprisingly, the results show relative confusion as to what a CDE is and how it should be articulated within a project setting. Low awareness of current tools and what constitutes a CDE, its required functions and its general deployment in practice were observed. A series of recommendations are formulated to better frame and raise awareness on the concept of CDEs, namely its potential benefits and uses cases. The results indicate that there is still a lot of work to be done to support the structured adoption of CDEs to ensure their successful implementation and use.

In 2017, Enwave Energy Corporation (Enwave) identified a need to expand their deep lake water cooling and hot water distribution network in the City of Toronto (Toronto). Enwave retained R.V. Anderson Associates Limited (RVA) to undertake project management, preliminary/detailed design, cost/schedule/risk management, stakeholder negotiation, and assistance during tendering/construction for this Western Expansion Project (WEP). Enwave’s primary objectives were to connect four (4) Enwave customers with chilled/hot water supply/return along the trunk pipe route, meet the contractual online service dates for the four (4) Enwave customers, place as much of the trunk pipe route within the ROW as possible, minimize impacts to existing stakeholders, minimize the construction cost by optimizing the trunk pipe route, and future-proof the system. The final design included a deep rock tunnel, shallow soil hand-mined tunnels, and shallow open-cut trenches. The total length of the route was 1400 linear metres of new chilled/hot water supply/return trunk pipe. The pipe sizes were 600 mm inner diameter (ID) HDPE for the chilled water trunk, and 350 mm ID FRP for the hot water trunk. Pressure relief chambers and valves were included at high points along the route, and buried isolation valves were installed at strategic locations for maintenance/operation and future expansion. In late 2021, the system was energized, and it is currently in operation. With a new thermal storage hub at The Well, Enwave is now capable of further expanding their district energy system. This paper presents some of the key design challenges and solutions that were developed to address the primary objectives. This includes a discussion on information gathering and optimization of the trunk pipe route. The goal of this paper is to give owners/designers some tools to help them navigate a complex design project like the WEP in Toronto.

The increase in urbanization rates has driven the construction of highly complex infrastructural projects across the globe, including the construction of bridges. However, such growth comes at an extremely high cost as the industry is considered to be among the most dangerous industries. Accordingly, construction safety has been raising several concerns due to the associated adverse impacts, thereby creating an urge to depart from the memorization and spoon-feeding approaches of the traditional safety training methods to novel teaching methods that incorporate new technologies such as virtual reality (VR) and augmented reality (AR). The efficacy of using VR-based training programs within the construction industry has been well established in the literature; yet there is still a lack of the simultaneous capitalization of several technologies to push the limits of the provided training programs in relation to the trainees’ learning outcomes. Such a lack has been coupled with a lack in targeting the hazards associated with one of the riskiest construction project types, namely the construction of bridges. Thus, this research aims to design and test a safety training program that integrates AR and VR to address potential hazards in the construction of bridges in an attempt to introduce novel training means that would elevate the quality of training programs within the industry. It is worth noting that this research is a part of a mega-research that aims to develop a comprehensive AR and VR-based bridge construction safety training program. The results revealed a statistically significant improvement in the trainees’ knowledge acquisition, safety motivation, safety awareness, and hazard identification and assessment skills. Hence, not only do the results of this research aid in enhancing the safety performances of bridge construction projects but also, act as a basis for the development of novel training approaches to elevate the learning experiences and gains of trainees.

The construction industry has high rate of accidents and poor safety records around the world. In addition to the injuries and fatalities, accidents affect both time and cost of the projects, thereby leading to time delays and additional costs. Previous research studies indicate that poor training contributes to the majority of accidents in the construction sector. Traditional training methods are inefficient in inducing the desired learning outcomes due to the passivity of knowledge receivers. Accordingly, new experimental and proactive learning approaches have emerged, out of which the safest and most ethical is the virtual reality (VR) construction safety training. VR safety training enables users to simulate reality in 360 degrees at the full visual capacity and to react accordingly improving understanding and critical response to stimuli. The research stream in this area is still relatively not fully explored, and previous work mainly focuses on semi-immersive VR technologies. The goal of this research is to develop and test a fully immersive and interactive VR model for safety training in accordance with the Institution of Occupational Safety and Health (IOSH). The focus is on advancing hazard recognition and mitigation “skills” rather than just providing “information”. The VR model was developed and deployed using state-of-the-art technologies, with a focus on falls, struck-by, slips, and general site safety. A testing phase was then initiated throughout interviewing professional safety inspectors to validate the model capabilities and to give their insight into approaches for further development. After that, the model was tested on senior university construction engineering students, and their performance was compared to students who didn’t take the VR training. Interviews indicated that the developed model would be of great importance for entry level workers and for general safety inductions; in addition, it can be more developed to target professionals on training centers. Experimental testing indicated statistical significance in the effectiveness of the developed model in enhancing students’ understanding and visualization. In addition, when compared to other available VR models, the developed model performed better in terms of visualization, immersion, realism, and ability to enhance the desired hazard identification and mitigation skills.

Quality assurance (QA) plays an essential role in the construction project life cycle. During the construction phase, discrepancies between as-built structures and as-designed models can lead to schedule delays and cost overruns. Currently, QA for buildings is primarily conducted by inspectors physically touring the building to visually inspect and manually measure discrepancies between the design model and the finished structure. This manual approach is time-consuming, costly, and error prone. In this study, we proposed a vision-based approach toward automated QA using images collected via virtual cameras in a game engine. Specifically, our approach aimed to address the problem of the lack of large-scale labeled open datasets for training reliable machine learning models for the task of semantic segmentation of building components (i.e., labeling of each pixel to a specific class of building component). The approach leveraged Building Information Modeling (BIM) authoring tools and a game engine to automatically generate images of virtual buildings with pixel-wise labels. Given the labeled images, a convolutional neural network (CNN)-based model can be implemented for accurate segmentation of the images. To validate the approach, we used one building information model as the testbed. In total, 20,700 images (18,000 for training, 2200 for validation and 500 for testing) were generated from the BIM. Performance of the CNN segmentation model was measured by the mean Intersection over Union (MIoU), which achieved 0.89. The result is significant since it rivals the current state-of-the-art from the Architecture Engineering and Construction (AEC) domain. The approach proposed in this study lays a concrete step toward automated QA, where inspectors can leverage the trained CNN model to automatically label images collected onsite during or after construction to avoid labor-intensive manual inspections.

The use of Building Information Modeling (BIM) is rapidly gaining popularity among designers, owners, and construction project managers thanks to its ability to generate and communicate construction-related information. However, the use of BIM to provide job-related details to construction workers is limited so far. There is a need for tools such as augmented reality (AR) applications to enable the BIM information flow to field workers to benefit fully from it. Due to a lack of guidelines, there is a need to provide AR development resources to potential developers, such as construction professionals, construction educators, and new AR developers. This article describes the development of a marker-based AR application for handheld devices to deliver just-in-time information to construction workers. The resulting application aims to help workers to learn task-specific construction methods and safety information before and during task performance by interacting with the models of the task at hand. The authors collected the proposed AR application requirements through a literature survey. The application was developed in the Unity game engine using the Vuforia AR software development kit. Three-dimensional BIM models and simulations of building elements, such as slab-on-grade reinforcement mesh and formwork models, were developed in Autodesk Revit and Autodesk Navisworks. Models and simulations can be visualized in the marker-based AR application. The application development process and resources documented in this paper will help new developers, especially non-programmers, avoid the pitfalls that the authors experienced during the development of this application.

In modern construction, the construction sites are congested, busy, and full of obstacles. The environment in the construction site is dynamic. In recent times, deep learning-based models remain the main tools used for image classification. However, the performance of the deep learning models in construction site-related image classification is not convincing due to the dynamic nature and busyness of the construction sites. The availability of construction site-related image datasets also remains an obstacle in achieving the best performance from the deep learning models. Data augmentation is a technique used to apply random but realistic transformation to the images. Data augmentation will not only help to diversify the image dataset but also assist in increasing the size of the dataset. This study used a state-of-the-art YOLOv4 deep learning model and implemented data augmentation techniques like gamma transformation to control the intensity of light and mimic sunny, cloudy, day, and night situations in the construction site images. The other data augmentation technique used is Gaussian blur to minimize the details in the images, and salt-and-pepper noise to degrade the quality of construction site images. The model is trained and tested on Alberta Construction Image Dataset (ACID) and construction workers hand signal image datasets, with and without the implementation of data augmentation. The performance of the model is evaluated based on the test dataset while keeping all the parameters of the model the same. It is observed that the model trained on the augmented dataset performed better than the model trained on the non-augmented dataset by 4%.

Robots can support onsite workers with repetitive and physically demanding tasks (e.g., bricklaying) to reduce workers’ risk of injuries. Central to the wide application of construction robots is solving the task of motion planning (i.e., moving objects optimally from one location to another under constraints such as joint angle limits). Currently, robots are mostly deployed in the manufacturing phase of a construction project for off-site production of building components. Motions of these robots are pre-programmed and follow strictly designed trajectories and actions. However, the motions of robots on construction sites require considerations of uncertainties, including the onsite movement of material and equipment, as well as changes to workpieces and target locations of the work piece. Therefore, it is essential to enable construction robots to handle these uncertainties while executing construction tasks to extend their applicability onsite. In this study, we proposed an integrated approach combining virtual environments and reinforcement learning (RL) to train robot control algorithms for construction tasks. We first created a virtual construction site using a game engine, which allows for the realistic simulation of robot movements. Next, the physical characteristics of the workpiece (e.g., location) were randomized in the virtual environment to simulate onsite uncertainties. An RL-based robot control algorithm (i.e., Proximal Policy Optimization) was implemented to train the robot for completing a construction task. We tested the robustness and effectiveness of the approach using a testbed construction site for window installation. Results showed that the proposed approach is effective in training the construction robot arm to handle window installation under the uncertainties of window location, with a success rate of 75% for picking up (i.e., grasping) the window and a success rate of 68% for placing the window to its target placement without crashing into other objects onsite. Researchers and practitioners can use the proposed approach to train control algorithms for their specific construction tasks to allow for flexible robot actions considering onsite uncertainties.

Mixed reality (MR) and virtual reality (VR) technologies are frequently recommended to present design models of construction projects to perform various visualization tasks, such as improving hazard identification training and constructability analysis. Significant financial investment is commonly highlighted as a major impediment to using these technologies, particularly MR. The cost of purchasing head-mounted display (HMD) hardware, such as Microsoft HoloLens for MR, Oculus Quest, or HTC Vive for VR, is substantial. However, the Oculus Quest manufacturer states that, due to recent software improvements, users can now deploy and run both MR and VR applications on the same Oculus Quest HMD, which was initially built only for the VR environment. This development appears to provide significant financial savings to those in the Architecture, Engineering and Construction (AEC) industry who are interested in gaining access to MR and VR. This article explores the possibility of using the Oculus Quest device to deploy MR and VR environments in construction. First, 3D models required for the experiment were created using Autodesk Revit. Then two applications were created using the Unity game engine, the Mixed Reality Toolkit for MR, and the Oculus Quest software development kit for VR. To ensure that the results were comparable, only one type of device and one construction task model were used in MR and VR settings. The MR and VR functions of Oculus Quest HMD were tested. Data on performance and user feedback were collected and analyzed. MR was easier to use than VR for this particular experiment because MR allowed the user to walk freely and view the room in real time, better understand the task, and get better details, accuracy, and scale. This way, one device can provide both environments, MR and VR.

Virtual reality (VR) technology has gained wide momentum over the past few years. Accordingly, it is rapidly becoming a solid foundation for existing and future educational programs in medical and engineering fields. VR aids in the provision of a student-centered learning approach; creating learning experiences that were not otherwise possible in real life. In the construction industry, the use of VR showed significant potential in enabling the development of a preventative culture in safety training programs through enhanced hazard identification, assessment, response, and mitigation skills. However, none of the few existing attempts—albeit beneficial—has considered major learning theories in the design of their VR-based training. In fact, the understanding of adult learning processes is a matter of serious concern for scholars and practitioners advocating any educational reforms. This concern has been exacerbated with the increasing advocacy for incorporating learning theory foundations in game-based learning. In line with this, this research aims to develop a conceptual framework for the integration of social learning theories in the design and development of VR-based safety training programs within the construction industry. Accordingly, six main theories were analyzed to identify the main principles that are most beneficial to VR-based safety training models. In doing so, major adult learning attributes, such as self-direction, problem/life-centeredness, learning orientations, reflective learning, and others, were taken into account. The results revealed that combining behaviorism, constructivism, and andragogy would tap into all the learning needs of adult learners, thereby aiding in the development of self-actualized individuals who are capable of acquiring declarative and procedural knowledge while adopting new problem-solving skills. They also reveal the importance of stimulating emotions and linking them to hazards. This research will help in significantly improving future VR training models and amplifying their desired outcomes, which in turn will enhance safety in construction sites.

The uncertain nature of construction processes and the variability of utilized resources make resource deployment planning a complex process, specially on an organizational level. Multiple operations modes of resources add even more dimensions to the planning and decision-making process of resources deployment. In scattered short-term repetitive projects, the frequency of decisions related to resources allocation and deployment constitutes a logistics burden on any organization. Conventional Planning tools are time consuming and fall short of meeting the periodical need to update resources deployment plans required in scattered repetitive projects. This paper presents a new outlook on construction operations in scattered repetitive projects and introduces the basis of an optimization model to minimize the time and cost of these operations on an organizational level. The model utilizes Genetic Algorithms (GA) and stochastic simulations as an adaptation of two famous problems in literature: the Multiple Traveling Salesman problem (MTSP) and the Multimode Resource-Constrained Time–Cost Tradeoff Problem of Repetitive Projects (MRCTCTP-RP). The proposed model considers both aspects of resources allocation and mobility between projects in optimizing the minimum time and cost of projects on an organizational level.

Modular construction, as an alternative to traditional stick-built construction, allows for schedule overlapping and efficient utilization of resources. In Canada, modular construction techniques are applicable to northern sites where labour and materials are scarce, the shipping season is short, and the method minimizes exposure to harsh winter conditions. However, when a modular construction supply chain gets longer and increasingly complex, analysing and managing it becomes a challenge. Additionally, there is a lack of research in modular scheduling compared to research in production. This paper introduces a case study where a supply chain model with manufacturing coordination was created and applied to analyse the shipment of housing modules from a factory in Southern Canada to communities in Nunavut. The case study was completed in partnership with Nunafab, a company trying to help solve Nunavut’s housing crisis using modular construction. The model uses a pull-based approach, where the inputs are desired completion dates and information about the supply chain and the output is a schedule for module shipment. A discrete event simulation is also used to estimate manufacturing output at the beginning of the supply chain. The results show that the model can replicate a single season of housing module shipment, and work improvement performed on the factory reveals the opportunity to deliver more modules in less time. While having limitations, the proposed model is simple and can be generically applied to analyse similar supply chains. The case study highlights the importance of coordinating modular manufacturing with supply chain research when performing work improvement, and it has led to further collaboration between the researchers and Nunafab.

The increasing complexity of construction projects combined with the inefficiencies in conventional construction management techniques has led to unnecessarily elongated payment cycles and other payment problems which can adversely affect the performance of projects and involved stakeholders. The rising concerns around construction payment problems have led to the introduction of prompt payment legislation in various jurisdictions worldwide with the common objective of increasing cashflow down the construction pyramid in a fair and reasonable manner. While prompt payment legislation aims to alleviate payment problems, their impacts and challenges remain unclear. In light of this, the main objective of this paper is to investigate construction payment problems, their common root causes, impacts, and potential mitigative options. Moreover, the paper also provides a critical review of prompt payment legislation introduced and implemented in different international jurisdictions for addressing the timeliness of payment in the construction industry. This review was followed by an analysis of the challenges in the implementation of such legislation as well as recommendations for effectively addressing them. Finally, this research can be utilized as a valuable resource for various jurisdictions who wish to address construction payment problems by introducing and effectively implementing prompt payment legislation.

Over the past decades, research has documented the profound changes needed in the way projects are delivered in the architecture, engineering and construction (AEC) industry to improve the outcomes of construction projects. Recent studies show that an increase in collaboration between parties makes it more likely to lead to successful results in terms of project goals, schedule, cost and quality. Within that context, owners have moved toward collaborative approaches to project delivery by using relational contracting methods such as Integrated Project Delivery (IPD). IPD can be distinguished by the use of integrated agreements, being minimally agreed between owner, professional and contractors. It can be defined as a project delivery approach that integrates people and practices by aligning the incentives and goals of the project team through shared risk, shared reward principles, in addition to early involvement of all parties. In response to growing demand, the Canadian Construction Documents Committee (“CCDC”) created the first-ever Canadian tri-party agreement in 2018, the CCDC 30. Although this contract form has already been used a few times in Canada, it remains fairly new, and some owners indicated that it has some shortcomings when compared to other IPD contracts. Through literature review, existing project documentation and semistructured interviews, this research seeks to identify the key differences between the recent CCDC 30 and other existing, more mature IPD contract forms and how these might inform the improvement to CCDC 30. The study reveals that an improvement in the language used within the contractual agreement, coupled with enhanced definitions would make a significant improvement to the standardized form. It also showed that there is a substantial need for additional guidance to support the means and methods needed to successfully perform an IPD project.

Site layout planning (SLP) refers to the efficient allocation of temporary facilities on construction sites. SLP especially becomes important when space is scarce in heavily congested sites. Previous research has mainly focused on the optimization of the locations of resources in site layouts. However, despite the achievements made, there is no evidence of the application of these tools in practice. This issue arises from little or no interest in the SLP during the early phases of the projects; a high level of sophistication across optimization tools; difficulty in standardizing optimization models such that they can be used in different projects, and the extensive times required to instruct practitioners in the use of such tools. These causes suggest a potential misalignment between currently available tools and actual requirements from industry practitioners. The long-term goal of this study is to define the requirements and specifications for an SLP tool that is deemed practical to industry practitioners. As an initial step, the objective of this paper is to investigate the decision-making process applied by practitioners during the SLP task. Data will be collected using structured interviews and analyzed through qualitative coding techniques. The results of this study will set the foundations of the SLP decision-making process as done in practice. Ultimately, this will lead to developing the attributes required in an SLP tool that can address the needs of practitioners in the construction industry and potential research opportunities for further developing the SLP tool.

When site space is limited, construction site layout planning plays a critical role in the safety and productivity of all operations. As the construction progresses, dynamic planning for the site layout becomes a need to match the project progress. Proper layout planning should take into account the productivity of operations, minimizing the in-situ travel time, the dynamic nature of site operations, and safety regulations implementation on site. This paper is a continuation of the previous work tackling irregular site layout planning, where it presents a new model approach for optimizing the dynamic planning of site layout with safety consideration. This model’s algorithm shows high performance in tackling complex projects with limited run time compared to other work in literature. The model showcases an automated mapping tool in conjunction with a dynamic scheduling observing safety to produce the optimized site layout using genetic algorithms. To demonstrate the benefits of the proposed model approach, the case study presented in the “Dynamic Layout of Construction Temporary Facilities Considering Safety” paper is taken as the performance reference to verify and validate the model’s output. Examination of the results and the comparative analysis is performed to demonstrate the variance between the outputs of the existing reference model, and this model performed herein.

The Gordie Howe International Bridge (GHIB) construction actively commenced for all components in Summer, 2019. On all projects and especially large one’s like GHIB, there needs to be a place for all documents and that place needs to be managed. This is typically accomplished by an Electronic Document Management System (EDMS). In 2021, Physical Filing Cabinets and Storage Rooms have been replaced with Digital Folders on Cloud Based Servers. This ‘data in the sky’ is by far more secure than any physical filing of yesteryear. On GHIB, there are the typical documents, Design Drawings, Specifications, RFI’s, Working Drawings, Record Drawings, etc. Bridging North America (BNA) has utilized the Autodesk BIM360 platform to manage notifications and documents. BNA and AECOM have collaborated and developed workflows to process and manage each type of document. Interactive forms were developed by AECOM to facilitate documentation flows. As with any project there is the need to find the right solution. The digital world is constantly changing, and adaptation is ongoing. During the construction phase of any project, there are expectations of the need to process contractor’s questions, review of third-party drawings for the undertaking of the works, procedures, and other plans. The magnitude and complexity of the scope of the GHIB project generates a significant volume of construction related submissions to be processed by the respective Engineers of Record. There are two genres of submittals: Field Clarification Requests and Working Drawings. The Working Drawing submittals are further subdivided into multiple sub-categories. The AECOM Construction Phase Services Leads are the first point of contact for submissions and provide triage for determining the needed resources from the applicable discipline Engineer of Record. AECOM utilizes Bentley’s ProjectWise to provide the repository for submissions to be processed and then archived. The volume of submissions increased in direct proportion to the extent of works being undertaken on the various components. Confirmation that the correct process has been undertaken is certified by the Construction Phase Services Design Quality Manager.

This paper describes a low regret-based adaptive decision-making methodology for evaluating alternative design and corrosion management strategies for steel roadway bridges despite limited site-specific knowledge of the actual corrosiveness of the environment. To illustrate the method, an example is provided, in which design options include whether using metallizing or not as a corrosion protection measure and its time of application. While in a mild environment, unpainted weathering steel might show little degradation, in case of realization of a severe environment, for example, due to extensive use of de-icing salt during winter, using this material without protection can be problematic as doing so might result in higher management costs or even catastrophic failures. On the contrary, the realization of a mild or moderate environment after utilization of the expensive galvanized/metallized steel or early adaptation of preventive measures can also be viewed as suboptimal. Another strategy is resorting to adaptive solutions, beginning with a less expensive option until the actual environment is more evident. Willing to minimize the “maximum sense of loss”, in the methodology applied here, regret is combined with the real option methodology and implemented in the decision-making framework. In this way, the methodology quantifies the desire of decision-makers to minimize the sense of loss associated with having made the wrong decision.

Site planning is always performed on construction sites to arrange the locations of facilities to minimize travel distances of the different resources. However, this type of planning and optimization does not consider the clashes and the congestions that may happen due to the presence of multiple types of resources. This results in safety hazards and injuries and loss of productivity in congested areas, leading to delays in the original schedule. The objective of this paper is to track the actual paths where resources are followed, starting from entering the site, moving to the desired location, performing work, moving to another location, and exiting the site. A simulation model is created using AnyLogic software to track the path of every single resource and produce a density map combining all of them, highlighting the different congestion areas. The model focuses on three types of resources, namely (1) labor, (2) equipment, and (3) material. After analyzing the congestions and conflicts between the resources, the model produces a safety analysis. In addition, the model helps identify the actual productivity rates of each resource, taking into consideration the travel time and delays on the site, hence producing an updated schedule. Finally, during the execution of the work, online tracking of resources may be used to compare it against the model to identify loss of productivity between the resources on-site. The main aim of this paper is to design the framework and the model that will further be used for the analysis.

The increasing complexity and magnitude of the projects impose greater impact of delays on stakeholders. Construction delays are a major source of disputes in construction projects. Since a construction project depends on interactions and shared responsibilities among parties, research works were directed towards identifying delay causes, quantifying their impacts, and proposing ways to deal with them. Different delay analysis techniques (DATs) applied to the same project’s delays provide different results, and thus, the selection of technique to use in evaluating delays becomes vital. Reviewing the literature, it has been realized that often there are disagreements, which lead to escalating a claim into a dispute, between engineers and contractors on the technique to be used to evaluate the delay. A dispute results in additional costs, time, and in some cases negatively impacts the relation between the parties as it goes up the ladder of dispute resolution. Some research was conducted to gather experts’ opinions on the best technique to be used; however, little research was done to quantify factors behind the selection and transform them into a numerical model. This research is an attempt to support different parties in selecting the most appropriate DAT to be used for a delay by building a model based on quantifying experts’ opinions to score different factors influencing the selection of DATs. Moreover, a survey based on the Egyptian market was conducted and used to build the model. Results of the survey were compared to surveys, from different countries, that tackle those different factors. This research helps in integrating the efforts that were exerted to tackle this challenge while providing analysis of how different factors are perceived through different law systems. Delay analysts, contract administrators, and other parties can use the model to validate their chosen DAT for a claim.

Precast concrete construction has shown great potential as an alternative to traditional in situ construction methods, as it supports a high level of industrialization, low dependency on labour, and significant benefits in terms of sustainability and environmental performance. In spite of these widely recognized advantages, though, it is still in its infancy compared to other manufacturing sectors. Numerous studies have been conducted to improve management in precast concrete construction through scheduling models, supply chain optimization, information technology applications, and integration of lean. However, the design of the production factories in which most of the work associated with precast concrete construction is carried out has been overlooked in the existing literature in this area. Due to the bulky size of prefabricated products and related parts, their flowing inside the factory requires equipment and manpower. Well-designed in-plant logistics can boost production efficiency and reduce material handling costs (which have been reported to account for a significant proportion of production cost in precast concrete construction). In the research presented in this paper, axiomatic design (AD) is applied to optimize the factory logistics in precast concrete production to achieve the concept of “right part mix to the right place at the right time”. The aim of this paper, then, is to propose a framework that seeks complete optimization via axiomatic design by converting customer attributes into functional requirements and deriving the design parameters based on the functional requirements in a “zigzagging” process. In such a way, factory logistics activities such as parts feeding, material handling, and even the addition of functional departments can be designed and integrated based on the actual production requirements. To the best of the authors’ knowledge, the holistic design of precast concrete construction factory logistics has not been investigated in previous studies; moreover, the evaluation results presented in this paper demonstrate the effectiveness of the proposed optimization framework.

The risk of project delay is a common phenomenon with an adverse effect on the performance of projects in the construction sector. The effect of its negative impacts in terms of cost overruns, reduced quality, and productivity extends to the owner, consultant, and contractor. The goal of this paper is to introduce an agent-based simulation model for the risk analysis of the project schedule component of construction projects, based on three risk management decisions. In the simulated model, the authors indicate four main phases in the construction process, along with their approval stages: (1) handing over; (2) engineering; (3) procurement; and (4) construction, which are commonly subjected to delays in the completion of the required activities. In addition, the developed simulation model should allow decision-makers to explore the impact of risks on the project schedule, in terms of schedule and cost overrun, based on two risk-response controls: acceptance or mitigation. As such, three simulation models are formulated: (A) no risks; (B) with risks; and (C) with mitigation. The model has been run based on a 70% mitigation value. The results indicate rational values. Since the duration for the risks for the ‘with risk’ scenario resulted in the highest time, followed by the ‘with mitigation’ scenario, the lowest time is recorded for the ‘no risks’. Similarly, the highest cost is recorded for the ‘with mitigation’ scenario, followed by the ‘with risks’, ending with the ‘no risks’ scenario. Further validation tools signified the effectiveness of the mitigation decision on the recorded results. This is demonstrated by the sudden drop as the mitigation value has been decreased based on the user’s input.

Site advance methods for planning and scheduling are essential for effective project management. Typically, construction managers tend to produce schedules based on two constraints: (1) Minimize Project Duration and (2) Allocate Minimum Resources. Moreover, deficits in the cash flow result in reduced profits at the end of the project and delays if financing problems arise, which results in damages (usually additional costs). Traditional scheduling tools like the Critical Path Method (CPM) and Time Constrained Project Scheduling Problem (TCPSP) do not show high efficiency in achieving the required objectives, as scheduling gets complicated. Advanced planning and scheduling methods will be used to produce a feasible schedule that meets specific objectives. This paper proposes a multi-objective model, (1) minimum project duration, (2) resource availability, and (3) minimum cash flow deficit. The model is divided into two modules. The first module produces an optimized, automated schedule achieving the objective of minimum project duration. The second module applies an optimized resource constraint scheduling using user input data of the available resources to allocate the resources on each activity while maintaining the maximum number of resources on site. The model is optimized using an evolutionary algorithm, namely: Genetic Algorithm (GA).

Buildings consume 40% of the energy and contribute significantly to the world’s GHG emissions (Cao et al. in Energy Build 128:198–213 [1]; Ürge-Vorsatz et al. in Renew Sustain Energy Rev 41:85–98 [2]). Although the majority of the building energy in the world is consumed for electricity, 17% of the global population in 2017 lived without electricity grid connectivity (Das et al. in Appl Energy 196:18–33 [3]). Most of this off-grid population relies on fossil fuel combustion to generate electricity, heating, and cooling energy, while many countries such as Canada try to reduce GHG emissions. In Canada, Northern territories comprise one-third of off-grid communities (Canada Energy Regulator in Market snapshot: overcoming the challenges of powering Canada’s off-grid communities [4]). Approximately two-thirds of the Arctic and Northern communities in Canada rely on diesel (Canada’s Arctic and Northern Policy framework [5]), which reduces the energy affordability and security in the residents in Northern regions. Exploiting the local renewable energy sources may reduce the energy operating costs and improve the local economy and energy security. However, it is necessary to consider the sustainability of implementing renewable energy sources. This study aimed to evaluate the environmental and economic sustainability of implementing renewable energy systems in off-grid residential buildings in the Northern region in Canada using a scenario-based assessment. The study conducted a life cycle GHG emission assessment, cost assessment, and discounted payback period analysis. The economic and environmental performances of energy system scenarios were aggregated using eco-efficiency parameters. The life cycle GHG emission assessment indicated that biomass heating systems can reduce GHG emissions by over 75%. Furthermore, implementing wind turbines can improve the GHG emissions savings by up to 96%. The life cycle cost analysis indicated that implementing renewable energy systems can significantly reduce the life cycle cost with acceptable payback periods due to substantial operational cost savings. Furthermore, the energy system that included biomass heating system and micro-wind turbine that supply 80% of the electricity requirement has the lowest life cycle cost for energy. However, the eco-efficiency assessment showed that implementing biomass-based heating systems has the highest viability compared to all the other energy system scenarios due to the higher investment costs of micro-wind turbine installation. The findings of this study will assist community developers, policymakers, and researchers in planning renewable integration in off-grid communities.

The building sector is at the frontline in consuming energy worldwide and a substantial contributor to the global carbon footprint. An energy-efficient building can perform a significant role in lowering the adverse impacts of greenhouse gas emissions. Buildings’ physical characteristics and occupants’ behavioural patterns intensely influence buildings’ energy usage. With wide-scale adoption of the work-from-home concept in the last few years, the hours spent in residential buildings have spiked drastically. Building energy simulations can determine the associated increase in residential energy use. This paper proposes a research methodology that can help evaluate the impact of occupancy time on energy upgrades selection. This paper considers three levels of occupancy patterns, 25%, 50%, and 80% time spent at home and performance of three upgrades: heating, ventilation, and air conditioning system (HVAC), wall insulation (WL), and solar panels (PV), as well as their combinations. The performance is assessed in terms of carbon emissions and energy consumption costs. A real-life case study, a single-family detached home in Okanagan, Canada, demonstrates this methodology. The energy upgrade combinations were ranked through a multi-criteria decision-making method that considered economic and environmental criteria. The results revealed that PV and WL respond well under economic criteria. On the contrary, when greenhouse gas emission is the primary concern, HVAC combined with WL or PV has superior performance. The findings of this study can assist the construction practitioners (such as builders, developers, designers) and homeowners in making informed decisions for energy upgrade selection based on occupancy patterns.

Architecture education must prepare future architects for the challenges of global warming. Therefore, performance-based designs, particularly those with an energy focus, should be understood and appreciated by architecture students. This is particularly essential considering the requirements of many countries to have net zero-energy new buildings within a decade and to retrofit old buildings to that standard within two to three decades. This paper presents an approach to teaching a course on the subject to architecture students in a college that is oriented to form-based designs. The paper discusses the pedagogical challenges and the author’s approach to overcoming them. It also identifies the learning outcomes to cover this broad subject from an architectural education point of view. The paper discusses the rational for the course structure and outlines the course contents in view of the pedagogical challenges. Emphasis is put on the role of an energy modeling software in visualizing the relationship between architectural design decisions and the energy performance of a building. The software is also used to guide students through a final project to retrofit of an existing building to reach a net zero-energy status. A survey of students who took the course one year earlier is presented to get feedback on the pedagogical value of the used approach.

Real-time monitoring and control of on-site resources is a developing field. In recent years, there has become a growing need for enhanced monitoring systems on construction sites using real-time technologies. Such technologies have been used heavily in fields like IT monitoring and healthcare industries. However, the construction industry is yet to seize the potential of deploying real-time monitoring systems on construction sites. Limited research has been done in the area of applying real-time monitoring, such as RFID tags and visual sensing, for the control of on-site safety performance and labor and earthmoving equipment productivity. Most of the studies delve into analyzing the data collected, with the data only used for verifying the accuracy of the used real-time monitoring technologies used. This study aims to uncover the potential outputs of utilizing real-time data collection and analysis of workers’ locations on a construction site. The outputs are achieved by developing a semi-automated framework that studies the real-time data collected. The framework is divided into two stages. First, site data is collected semi-manually, and real-time workers’ spatial data is collected using GPS tracking technologies. Second, the collected data is prepared for visual analysis. The proposed framework is then applied to a single case study for a commercial project in New Capital, Cairo, Egypt. Outputs from the case study reaffirm the feasibility of monitoring workers’ productivity by identifying their GPS locations in real-time on-site. The framework aims to emphasize the potential benefits gained from using real-time technologies on construction sites. Thus, this research provides stakeholders with a tool that collects and analyzes data from construction sites for improved monitoring and control of workers’ performance.

In recent years, the application of AM in construction has been increasingly studied. According to scientific literature, the use of robotic AM can help improve efficiency in construction by reducing the cost of completion, minimizing waste, decreasing lead times, and increasing profitability. Building Information Modeling (BIM), a digital technology widely adopted by the industry today, is known to boost construction productivity and quality. However, studies on the use of BIM in the different stages of AM are lacking in the scientific literature, even though there are major challenges to overcome in this area, including the lack of data interoperability between BIM platforms and additive manufacturing systems. In particular, BIM is almost solely used for modeling in the AM process, and other software is needed to process the 3D models and link them to AM robots. This research aims to close the gap between BIM data and AM. To achieve this, attention was paid to data interoperability between BIM platforms and automated building systems (a robotic arm) to preserve BIM model data throughout the manufacturing process. The proposed approach helps prevent the data loss of a 3D BIM model in an additive manufacturing process by using the parametric modeling tool Dynamo. A review of scientific publications, industry best practices, and academic laboratories research was conducted to identify current technologies and practices in the use of BIM in AM. Workflows were developed and tested on technical case studies using an operational framework for integrating AM processes with BIM. The studied cases represent a robotically 3D printout of native BIM models as a practical illustration. Results show that the use of robotic AM in the field of construction can contribute to improving efficiency, reducing the cost of completion, limiting waste, decreasing delays, increasing profitability, and avoiding the need to redo detailed work. It can also contribute to improve construction site safety and provide architects with more freedom of design and functional integration.

For several years now, the construction sector has been undergoing a digital transformation which is changing working methods. Companies in the construction sector must review and improve their internal processes to be more efficient and productive. Building Information Modelling (BIM) is among the increasingly popular technological approaches being employed in the sector. For construction projects using BIM, BIM coordinators very often require rapid and efficient access to information to respond to the various requests they receive from other project teams (site teams, subcontractors, engineering offices, etc.). For infrastructure projects, on the other hand, BIM is associated with geographic information systems in order to conduct spatial analysis. In the latter context, the present research project aimed to develop and implement a GIS-BIM tool to aid in BIM coordination in the case of a major infrastructure project in Montreal. The choice to develop an automated dashboard integrating GIS-BIM information was made following an internal needs analysis. The tool was mainly developed with Microsoft Power BI, using different data from Excel, Revit, ArcGIS, etc. Following the development of the tool, the dashboard was presented to and tested by various users to collect their opinions and thus confirm whether it responded well to the initial problem.

In spite of the significant amount of research conducted in recent years to improve the efficiency of Building Information Modeling (BIM)-based multidisciplinary coordination processes, unanticipated increases in cost and delays in construction projects still occur. To mitigate factors that hinder the efficiency of construction processes, the literature proposes several solution frameworks. Our work presents an overview of the literature pertaining to these efficiency solution frameworks to identify and address research directions in design conflict resolution. In the existing literature, obstacles having a greater impact on multidisciplinary BIM coordination are sorted into five categories: (i) process, (ii) actor, (iii) task, (iv) context, and (v) team. The implications of each of these categories are considered for separate phases of multidisciplinary coordination. Furthermore, efficiency solution schemes are studied, ranging from shared situational awareness to supervised and hybrid machine learning frameworks. Connections are then drawn between these obstacles, their solutions, and the coordination phases in which they are most applicable. To the best of our knowledge, this is the first work to present a consolidated overview of solution frameworks, and it is our hope that it will help researchers and BIM professionals identify the scope of current research and understand future research directions.

Green procurement has been a growing area of interest in public sector purchasing. The above has sparked an interest in ecolabels that provide verified environmental performance information. Green procurement and ecolabels are less observed in the construction sector due to data challenges, transparency, and implementation resources. Blockchain is a promising technology that can aid in ecolabel data verification. Combined with Building Information Modeling (BIM), blockchain can be used to ensure the authenticity of construction project data and the validity of sustainability claims. A comprehensive literature review has revealed that no previous research has used BIM nor blockchain for green procurement in the construction sector. This paper proposes a methodological framework for integrating entrusted ecolabels through blockchain for the green procurement of water supply infrastructure. BIM was used as the platform for integrating project data. The outcomes of this study will improve the transparency of green procurement while promoting cutting-edge technology in the construction sector. More importantly, the outcomes of this research support addressing data credibility and accuracy in eco-conscious decision-making.

Design changes are an unavoidable reality in the construction industry. After construction documents are released, changes may be made to them by different stakeholders for various reasons. Design changes must be well managed in accordance with a design change management (DCM) process to avoid time and cost overruns. Furthermore, the right tools must be used to facilitate the flow of documents and ensure that stakeholders have the most recent documents. Electronic document management systems (EDMSs) have been introduced to facilitate the storing, sharing, and tracking of documents in electronic format. This research aims to report on the real use of an EDMS in the construction industry as part of a DCM process. Six semi-structured interviews were conducted with a construction project director to document the DCM processes applied in two ongoing construction projects, one of which involved the use of an EDMS. The data collected served to compare the functionalities of the different tools used during the DCM processes. The main results show that traditional tools (e.g., PDF reader and email application) and the EDMSs adopted share some functionalities. However, the EDMS offers significant advantages in terms of collaboration and traceability.

Cost overruns in construction projects are currently one of the biggest problems in the industry. Factors causing these cost overruns have been identified in order to improve current management practices. Building Information Modeling (BIM), and in particular the fifth dimension relating to cost management (5D BIM), offers techniques and processes to improve the quality, performance, and efficiency of projects. Current BIM 5D practices are evolving and many software programs are being developed in this direction. However, due to the multiplicity of the proposed solutions and the wide range of the possibilities and associated functionalities, it is challenging for the practitioners to select the right tool adapted to their particular requirements. Previous research works have attempt to solve the issue by proposing some comparison of the existing 5D BIM tools. While very interesting, such comparisons are not sufficient since they do not provide the user with the level of interactivity necessary to adapt the choice to their particular business context. The research project presented in this paper proposes an interactive decision-support tool for the effective choice of 5D BIM solutions. The proposed tool is based on the characteristics and functions of the existing software and uses a personalized weighting by the user for each of the identified cost management requirements. Thus, the proposed tool makes it possible to inform users about 5D BIM software according to their specific requirements and to provide them with informed software selections. The results were evaluated and validated by experts in construction cost management.

Storm surge and waves are responsible for a substantial portion of the tropical and extratropical cyclones-induced damage in coastal areas of the USA and Canada. High-fidelity, numerical models can provide accurate simulation results of the water elevation, where a hydrodynamic model (e.g., ADCIRC) is coupled with a wave model (e.g., SWAN). However, they are computationally expensive, hence cannot be employed as part of an early warning system for urban flooding hazards or implemented in probabilistic tropical and extratropical cyclones’ risk assessment. In this study, an alternative and efficient approach is proposed based on hybrid machine learning approaches. First, a dimensionality reduction technique based on deep autoencoder is developed to encode the spatial information in a reduced state space. Then, a machine learning-based model is developed in the latent space to predict the maximum surge and significant wave height. The latent space is then decompressed back to the original high-dimensional space using the decoder. The high-fidelity data are retrieved from the North Atlantic Comprehensive Coastal Study (NACCS), released by the US Army Corps of Engineers. Due to its high efficiency and accuracy, the proposed methodology can be employed to analyze the impact of input uncertainties on the simulation results. Four machine learning algorithms are used to predict the maximum surge and significant wave height including artificial neural network (ANN), support vector regression (SVR), gradient boosting regression (GBR), and random forest regression (RFR). The coupled autoencoder-ANN model for the prediction of the storm surge (significant wave height) outperformed all other algorithms with a coefficient of determination R2 of 0.953 (0.921) for the testing set. In addition, the comparison between deep autoencoder and the widely used principal component analysis (PCA) technique indicated the superior performance of the former since it is able to accurately capture the inherent nonlinearities within the data.

The water–energy–carbon nexus is crucial to the consideration of renewable energies and sustainable developments when analyzing the intensive requirements of water services. Installing a turbine in key areas of water distribution networks to recover the wasted energy proves to be beneficial to counter waste such as leakages in pipes and pressure reductions. Previous studies have proposed models to predict characteristics of pumps as turbines (PaT) based on pump best efficiency point. These models apply statistical methods to determine the turbine characteristics, but accuracy in these models is still questionable. Data science and machine learning applications have become increasingly popular to show correlations in datasets and offer deeper insight in predicting the relationships of a PaT in pump and turbine modes. The goal of this study is to apply machine learning algorithms to model characteristic curves of PaTs. A database of over 140 PaT experimental records was compiled to compare machine learning results to that of the predicted values from the established models. Considering the limited number of features and data points, regression models were developed, including elastic-net, ridge regression, support vector regression, etc. The predicted results were evaluated based on the coefficient of determination (R2) as well as error according to the root mean squared error (RMSE) and mean absolute deviation (MAD). The results show the proposed method to be more reliable with smaller margins of error in predicting PaT characteristics than previous studies.

Channel-bed roughness has the influence on near-bed flow, with implications in hydraulic engineering design. Previous studies suggest that small-scale structures of turbulence are an important element for near-bed flow. However, very little research has dealt with turbulence structures of flow in the vicinity of roughness elements at the channel bed. The reasons for this include the high costs and technical difficulties to obtain fine resolution measurements of near-bed flow from natural and laboratory channels. Therefore, Computational Fluid Dynamics (CFD) models become an attractive tool for the investigation of near-bed flows. The purpose of this paper is to use Large Eddy Simulation (LES) as a mathematical model to predict eddy motions, including small-scale motions near the bed and around roughness elements. In this study, LES runs are performed under conditions matching a series of laboratory experiments. The LES runs use OpenFOAM as a numerical solver. The simulations numerically solve one-phase incompressible flow using the finite volume method and yield snapshots of instantaneous velocity and pressure fields. The simulations use the rigid lid approximation at the upper boundary of the model channel and apply cyclic conditions between its upstream and downstream lateral open boundaries. A significant challenge in LES is to resolve small-scale motions of dynamic importance. This paper handles the challenge by resolving the viscous sublayer. This treatment permits the use of no-slip condition, which is realistic. The ensemble averages of instantaneous velocities are validated using experimental data. The LES results are further analyzed to reveal eddy motions and turbulent flow patterns. The results provide details of turbulence structures near the bed with the presence of roughness elements.

This paper aims to calibrate, in real-time, a hydraulic model by combining an implicit technique with a genetic algorithm (GA). The approach uses demand multipliers as the calibration parameter. The hydraulic model yields an optimal solution through minimizing a nonlinear objective function between observed and simulated values of nodal pressures and pipe flow rates at a five-minute time interval, subject to a set of explicit bound constraints for the demand multipliers and implicit nonlinear hydraulic constraints. The hydraulic modeling software EPANET is applied to solve hydraulic constraints, including the equations of mass conservation for each junction and energy conservation for each pipe, and to retrieve the simulated values required in the objective function after assigning demand multipliers to nodes in the hydraulic model. The simulated values are computed under the same boundary conditions, including tank levels and pump status, as the measurements are collected. The approach is applied to the L-Town hypothetical network as a case study, yielding not only simulated values in good agreement with measurements but also consistent variation trends over time between simulated values and measurements. The case study shows that demand multipliers expressed in real numbers lead to more accurate simulated values, closer to the measurements, than expressed in a string of bits. Further efforts will focus on leak detection using the well-calibrated model.

Particle-laden turbulent jets have various engineering applications such as effluent discharge, wastewater disposal, and marine bed capping. The flow field of particle-laden swirling jets impinged into stagnant water was investigated using the combined high-speed imaging and Particle Image Velocimetry (PIV) measurements. A swirling chamber was designed with an inner diameter of 32 mm encompassed by six internal spiral water paths each having 4 mm inner diameters. Sand particles with a median diameter of 0.386 mm and an initial concentration of 60% were added through a funnel into the chamber to mix with the swirling water, and the mixture was discharged into stagnant water through an outlet nozzle with a diameter of do = 6 mm. The axial and radial distributions of velocity components were measured for two different swirling numbers of 0.50 and 0.65. It was found that sand–water swirling jets expanded moderately under induced recirculatory motion, formed a wider spray cone, and high radial velocity of sand particles. Due to formation of Precessing Vortex Core (PVC), the horizontal velocity profiles in the radial direction, r, experienced a near-field peak at r/do = 2. The axial velocity decay rate of jets with swirling numbers of 0.50 was found to be dissipated rapidly by increasing the swirling intensity from 0.50 to 0.65. The swirling motion increased the radial velocity distribution in comparison to the non-swirling sand–water jets. Two different clockwise and counterclockwise rotating vortices were observed throughout the recirculation zone which linked the internal and external shear layers. The flow oscillation and inter-scale dominant structures of the swirling jets were also investigated using the Spectral Proper Orthogonal Decomposition method (SPOD) and extracting energy-ranked spectra. It was found that a significant portion of turbulence kinetic energy of the jet was concentrated in the first five low-frequency modes.

The Lower Fraser River (LFR) refers to the Fraser River reach from the Trans-Canada Highway 1 Bridge at Hope, British Columbia to the river mouth at the Salish Sea. Its floodplains and the coastal areas adjacent to the river mouth are inhabited by approximately 3.1 million people. The LFR also has a long history of flooding. A two-dimensional (2D) hydrodynamic model was developed for the LFR using the software MIKE 21 model with flexible mesh. The model extent includes the LFR and its key tributaries–the Harrison and Pitt Rivers. It was calibrated and validated using the 1972, 2012, and 2017–2020 flood events with the flow range from 5000 m3/s to 13,000 m3/s. Two roughness scenarios were developed to adapt the flow range: the roughness scenario for low river levels and the roughness scenario for high river levels. The model results for the 2018 flood event were in excellent agreement with the observed data since the digital elevation model, which was used for the 2D model, was built based on the river bathymetry data surveyed in less than one year before the 2018 freshet. The model results for the other flood events were also in good (2012 and 2017—2020) or reasonable (1972) agreement with the observed data. The model will be used to calculate the LFR design flood profiles. It can also be used for many other LFR hydraulic modeling applications in a regional scale with a broad flow range.

The Pattullo Bridge over the Fraser River is the only major steel through-arch highway bridge remaining in British Columbia. Opened in 1937, the bridge replaced the narrow traffic lane above the railway tracks on the New Westminster Rail Bridge, improving vehicular traffic volumes. It connects the former BC capital of New Westminster with the region of Surrey and, along the Pacific Highway, the USA. Col. W. G. Swan (1885–1970) led the bridge design team, and the structural steel was fabricated and erected by the Dominion Bridge Company, both iconic names in the history of bridge engineering in BC. The Pattullo Bridge was the first major crossing into the Southern Greater Vancouver Area during a time of rapid societal expansion and has served a pivotal role, for almost 90 years, connecting suburban areas with metropolitan Vancouver. The impending replacement of the Pattullo Bridge heightens its historic importance: steel through-arch designs in BC, and so a part of British Columbia’s structural engineering history will become extinct.

The Britannia Mine, situated on the east shore of Howe Sound, 45 km (28 miles) north of Vancouver, produced more copper than any other mine in the British Empire between 1925 and 1930. Dr. A. A. Forbes originally discovered minerals there in 1888. When it ceased operations in 1974, it had produced over 517,000 metric tonnes (mt) of copper, 125,000 mt of zinc, and significant quantities of lead, cadmium, silver, gold and pyrite. The mine applied and improved a froth-flotation system that was particularly efficient in separating and concentrating the ore: the Britannia deep-cell flotation system helped triple the yield at one of their mills. The steep local mountain slopes were used to generate hydroelectric power that provided compressed air for the mine ventilation system, and to transport ore through the concentrator by gravity. Old rails were recycled to make grinding balls for the mills, many years before recycling technology and the circular economy were recognized to be desirable practices. They used IBM punch cards for time keeping in 1929. The mine also leaves an environmental legacy, however, as one of the largest sources of metal pollution in North America. Remediation efforts to protect Howe Sound and the Squamish River from acid rock drainage will be necessary for the foreseeable future.

In the era pre-1950, electrical energy in Saskatchewan was produced locally by municipalities that could afford to install electrical energy generation systems. In 1906, the City of Prince Albert, Saskatchewan gave consideration to developing a hydropower project on the North Saskatchewan River at a site located some 45 km downstream from the City. The site is known as La Colle Falls. The hope at the time was to encourage industry to develop in the City. The La Colle Falls project was conceived as a run-of-river hydropower project to be comprised of a low-height Ambursen style dam, a hydropower plant located a short distance downstream on a power canal, and a navigation lock to permit the passage of sternwheeler vessels on the river. Initial design work on the project started in 1909, with site investigations beginning in 1911. Construction commenced in 1912, but shortly thereafter the City ran into issues with financing for the project and work was stopped in August 1913. Although there were some attempts to get it going again the following year, the breakout of World War I essentially prevented any further progress. By this time, the City was almost bankrupt, but ultimately it was able to pay off the accumulated debt over the following 50 years. Today, the partially-completed works stand as a reminder of the often tenuous nature of engineering projects. In this paper, an overview is provided of the La Colle Falls project and of some of the key players involved with getting the project underway. Included are comments on the technical aspects of the project, such as the project location and style of dam, brief discussion of the several critical decisions that impacted the project’s success, and a brief summary of the project as it remains today.

This short submission highlights the activities of the CSCE National History Committee (NHC) since the previous update in 2020. New National and International Historic Sites have been created, specifically the Kinsol Trestle and David Thompson’s Surveying and Mapping of the Northwest of North America, respectively. Existing Historic Sites are now being regularly monitored. A major initiative has been rewriting the online descriptions of the Historic Sites using a standard template: The new descriptions together comprise roughly 55,000 words, 450 images, and 240 links to online information. Based on these new descriptions, weekly “Today in Canadian Civil Engineering History”, blurbs have appeared in the CSCE eBulletin since October 2020. The NHC is also participating in the Engineering Institute of Canada’s “Oral History Interviews to Preserve Canadian Engineering Achievements” initiative: in the summer of 2021, ten male and six female engineers were interviewed. The NHC has organized: tripartite webinars with the ASCE History and Heritage Committee and the ICE Panel on Historic Engineering Works; a special session commemorating the 80th anniversary of the construction of the Alaska Highway for the 2022 Whistler Conference; and a session on historic bridges for the 2022 Short and Medium Span Bridge Conference. Individuals interested in contributing to or participating with the CSCE National History Committee are warmly encouraged to contact the author.

The Cariboo Wagon Road was built by the Royal Engineers in the 1860s. The road linked Yale, the head of navigation on the Fraser River, with Barkerville, the center of the Cariboo gold rush. The Cariboo Road was the primary link between the Cariboo gold fields and the outside world for several decades. The Author’s Great Grandfather, the Reverend William V Sexsmith was the Wesleyan Methodist Minister in Barkerville from 1877 to 1881. In 1879 he traveled the length of the Cariboo Road from Barkerville to Yale and then by steamboat to Victoria to attend church meetings. He then traveled to Ontario by steam ship to San Francisco, CA, and railway to Napanee, ON. The purpose of the trip was to visit family (he was born in what is now Ontario) and more importantly to get married. He and his new bride then returned to Barkerville via the same route. Great Grandfather Sexsmith kept journals from time to time while he lived in British Columbia. One of those journals covers his 1879 trip from Barkerville to Victoria. During that trip, which took many days, he traveled by cutter, horseback, stage coach, steam boat, and on foot. Another journal covers the railway trip with his new bride from Napanee to San Francisco.

Ballantyne Pier was originally constructed between 1921 and 1923, as a major expansion to the Port of Vancouver to alleviate shortages of dock facilities, which had become sharply apparent after the First World War. The new pier also provided port access for the new Canadian National Railway, as the Vancouver shoreline had been dominated by the Canadian Pacific Railway up to this point. In addition to cargo-handling facilities, the new pier included the first grain elevator built for export of grain from Canada’s West Coast. The Ballantyne Pier shed’s façade was built in a three-bay brick and stone Classical Revival style, a popular style of the time, which was found in banks, schools, government offices and churches in that era. When completed in 1923, it was considered by some as one of the most technically advanced port facilities in the world. In 1935, Ballantyne Pier was the site of a clash between police and striking workers, an event known as the “Battle of Ballantyne Pier”, which ultimately led to the creation of the International Longshore and Warehouse Union (ILWU). Up until the early 1990s, Ballantyne Pier served as a bulk export facility. In 1992, the pier was redeveloped into a two-berth cruise ship terminal. Years later, the water lot between the west berth and adjacent Centennial Pier (Centerm) was filled in to allow expansion of Centerm’s container handling capacity. To meet continued growth in the Port of Vancouver’s container sector, the Vancouver Fraser Port Authority is once again redeveloping the combined Ballantyne Pier and Centerm sites. The historic Ballantyne Pier shed façade, including a portion of the original shed, is being retained and incorporated into Centerm’s new state-of-the-art container operations facility building.

Wildfires are considered one of the costliest natural hazards in Canada. Significant fire events that occurred had threatened and destroyed buildings at the Wildland Urban Interface (WUI). Standard methods for wildfire risk assessment include hazards’ analysis, inventory of exposed buildings and vulnerability analysis that correlates expected losses to fire intensity measure and distance from forest boundary. On the other hand, there is limited research on buildings’ vulnerability assessment to wildfire impacts and scarcity of models that correlate the likely response and expected loss of different types of buildings to varying levels of fire intensity. This article presents a methodology for geospatial data collection of post-fire buildings damage at Canadian WUI communities with the objective of developing community-scale empirical building fire vulnerability models that can be integrated in community-scale wildfire risk assessment tools. In this study, the empirical fire vulnerability model is developed in terms of the loss rate defined by the proportion of buildings burned as a percentage of the total exposed buildings as a function of the distance from forest edge and the corresponding fire intensity. The methodology consists of consecutive steps including geospatial digitization of burned and survived buildings from post-fire open-source satellite imagery; characterization of building types and occupancy based on open-source municipal databases; estimation of distances to burned forest boundary based on burn scar satellite imagery and the measurement of distance increments to buildings. The buildings data are then combined to develop an empirical fire vulnerability model. The methodology is demonstrated by a case study WUI community in Canada that was exposed to a damaging wildfire event.

In Canada, the development of fire codes and standards is based upon governmental resources aiming to advance requirements deemed insufficient or potentially non-reflective of current design trends. This has led to recent advancements of fire-safe engineered timber and urban interface protection from wildfires. It has at this time minimalized attention to the marginalized populations exposed to fire hazards, such as the numerous informal settlement fires observed across Canada in the last few years. Developing countries, generally, do not have the financial or administrative resources to advance their own fire codes and standards in such a manner. In general, developing countries adopt codes and standards from more established sources to fit their necessities. However, these codes and standards may not be wholly representative of their current design trends or fire problems. For example, in Costa Rica, adopting the National Fire Protection Association (NFPA) Standard 101 has caused confusion around the required fire protection needed for acceptable design as it was not created with Costa Rica in mind. The adopted NFPA code, more importantly, does not consider how to protect marginalized populations living in informal settlements. Informal settlements are a growing issue in both developing and developed countries. Informal settlements are at a greater risk of fire and other hazards due to the physical characteristics of the structures and lack of regulation thereof, socio-economic vulnerability of the residents, and the political and institutional marginalization of the settlements. Considering Costa Rica and Canada, the present study serves three purposes: to compare Developed (Canada) and Developing (Costa Rica) code advancement approaches; to determine code applicability to informal settlements; and to compare each country’s approach to informal settlements.

For the past few decades, many attempts have been made in order to develop infrastructure asset management models which simulate and optimize asset elements’ condition and maintenance plan. Organizations owning/operating hotels and resorts are of great need of asset management models to develop plans that efficiently manage their valuable asset to keep it in good conditions and achieve customer satisfaction. In addition, these plans should be within a certain budget and can accurately predict future deterioration/conditions of the different elements in the hotel/resort to keep on providing the required services. Several studies show that coastal structures have the highest deterioration rates due to harsh environmental exposure. In this research, an attempt has been made to develop an infrastructure asset management model for the Coastal Hotels and Resorts that can improve resource management and the overall condition of the asset. This model was built considering different factors that might affect the user cost or the required condition of the asset such as: star rating, occupancy. Elements’ qualities and types are used as input factors that predict the deterioration behavior of elements based on data acquired form literature and data collected from the field in form of surveys and inspection histories. The deterioration behaviors for the elements were determined by using different approaches; some were estimated using deterministic prediction models, while others were predicted using Markov chain or linear method. The model was applied on a case study and run for two scenarios: the first scenario was to minimize the total cost for the following 24 months while achieving a minimum overall condition; and the second was to maximize the overall condition with constrained budget. Finally, a life cycle cost analysis was conducted for the asset over a 10-year period to investigate the impact of the different material types and quality used in construction on the total life cycle cost.

The Canadian standard CSA A23.1:19 on concrete materials and methods of concrete construction addresses the concrete surface treatment in chapter 7 placing, finishing, and curing concrete and in chapter 8 concrete with special performance requirements. The factors affecting the abrasion resistance are specified. The surface defects are named as honeycombing, sand streaking, lift lines, variations in color, soft areas, and large surface void. Special finish of architectural formed concrete must minimize texture and color variations. The standard does not mention specific organic coating material. The standard is clear on specific forbidden action that can affect the surface finish. The International Concrete Repair Institute (ICRI) has more specific guidelines on the types of product to apply. The sealers and coating recommenders are classified as high molecular weight methacrylic sealing compounds, low viscosity epoxy sealing compounds, silane and siloxane sealing compounds, and coating compounds for concrete. The causes of damage to concrete are explained. The American Concrete Institute has a number of committees addressing surface finishing. The ACI 546.3R-14 Guide to materials selection for concrete repair has tables for sealers and anti-carbonatation coating selection based on durability factors.

Microplastic particles (MPs) have become interesting to the water research community and general public due to their global ubiquity and potential impacts on the environment and public health. MPs are plastic beads, fibers, and fragments in the micrometer size range (< 5 mm), which can be ingested by aquatic fauna across a range of feeding guilds. MPs result from a wide variety of sources, including fragmentation of larger plastics and laundering of synthetic fabrics. Previous studies on animals have indicated that ingestion of MPs could lead to serious health issues, including liver stress response and tumor formation. Through literature analyses and laboratory experiments, we explored microplastic removal in various conventional and advanced water treatment processes, such as coagulation, granular media filtration, and membrane filtration. This presentation speaks to the major MP compositions (materials, sizes, shapes, and concentrations) in drinking water sources and the removal efficiency of MPs in these different drinking water treatment (DWT) processes. Current DWT processes that are purposed for particle removal are generally effective in reducing MPs in water. Various influential factors to MP removal are discussed, such as coagulant type and dose, MP material, shape and size, and water quality. It is anticipated that better MP removal can be achieved by optimizing the treatment conditions. Moreover, this talk will frame the major challenges and future research directions on MPs and nanoplastics (NPs) in DWT.

For many small to mid-sized rural communities within Canada, municipal wastewater is treated within aerated lagoons, prior to being released back to the environment. Under changing provincial and federal regulations, these communities are required to treat their wastewater to a much higher standard than what lagoons can typically achieve. However, for many of these communities, the existing lagoons are still functioning well and represent a significant prior investment by the community. Communities are left with big decisions, such as abandoning their existing assets in favor of fully mechanical wastewater treatment plants (WWTPs), or connecting to a regional wastewater system (not feasible when the community is located a far distance away from an existing facility). In addition to significant capital costs, the ongoing operating costs for these systems can be significant as well, particularly when considering operator certification requirements for fully mechanical WWTPs. The City of Camrose (population of ~ 20,000) began initial design on its WWTP upgrade project in late 2008. After extensive testing of the City’s wastewater and the downstream receiving environment, in 2012, the City received confirmation from the provincial and federal regulators regarding the treatment standards that would be applied to the upgraded WWTP. In addition to considering more traditional mechanical wastewater treatment options, in 2015, the City received approval from the regulators to consider “hybrid” systems, which could leverage the investment of the existing aerated lagoons while minimizing the complexity and operator requirements of the upgraded WWTP. The City’s technology selection process involved many discussions with regulators, as some of the proposed technologies had not been used within Alberta. Ultimately, in 2019, the Province approved the City to proceed with detailed design on a hybrid WWTP that would see the existing aerated lagoons supplemented with a Moving Bed Biofilm Reactor (MBBR). The following report will highlight the process that the City followed to select the MBBR technology for its WWTP upgrades (currently under construction). Once complete, the Camrose WWTP will be the first facility in Alberta to use a full-scale MBBR treatment process within a hybrid WWTP.

The occurrence of high concentrations of arsenic (As) in water has been recognized as a global health and environmental problem. Sorption is regarded as a promising As treatment method due to its simplicity and potential for high efficiency. Canada has a strong agricultural industry that produces waste products that can be converted to value-added products. Considering the availability of agricultural residue in Canada, the cost of the sorption process can be decreased by using agricultural residue-based sorbents (biosorbents) as an eco-friendly alternative for commercial sorbents. In this study, sorption of arsenite, As(III), from aqueous solutions onto Fe oxide-modified canola straw (MCS) was investigated. The results showed that the negligible As(III) sorption capacity of raw canola straw increased significantly to 791 µg/g after modification in the removal of As(III) from a 1000 µg/L solution. Studying the effect of solution pH showed that As(III) sorption capacity of MCS increased by increasing the solution pH from 3 to 10. A kinetic study showed that about 66% of the ultimate sorption capacity was reached within four hours. The sorption kinetic data was best represented by pseudo-second-order and Elovich models suggesting that chemisorption may be the rate-determining step of the sorption process. The isothermal data of As(III) sorption followed Freundlich and Redlich–Peterson models indicating a hybrid adsorption mechanism with a higher probability of a multilayer heterogeneous adsorption. Studying the effect of co-existing anions in the solution upon the As(III) removal efficiency of MCS indicated a significant antagonistic impact of selenate (SeO42−), selenite (SeO32−), and phosphate (PO43−). However, the effect of nitrate (NO3−) and chloride (Cl−) on As(III) removal efficiency was insignificant, indicating that inner-sphere complexation was the leading mechanism in As(III) sorption.

Blackwater and greywater are transported through the same sewage pipe in the traditional wastewater network in a city. This mixture turns both the blackwater and greywater into blackwater, which needs to go through extensive treatment processes before being discharged. On the other hand, greywater can be reused for numerous purposes, even after basic filtration. Fortunately, there are varieties of household greywater reuse systems (GWS) available in the market that can make good use of this resource by reusing this massive amount of generated greywater with its internal filtration or UV disinfection unit. This stops this massive amount of greywater from leaving the household, eventually reducing the sewage bill drastically. Furthermore, the lower volume of blackwater also allows for smaller sewer pipes and reduces the number of wastewater treatment plants required in a city. These reduced wastewater treatment plants will result in a significant drop in cost per capita for the city’s population, as well as less environmental impact. Altogether, it can be affirmed that the outputs of this wastewater management system will satisfy the economic, environmental, and social concerns for sustainable cities. However, dual plumbing and greywater system installation, operation, maintenance, and replacement (OMR) costs will initially add new expenses to the bills. Therefore, an economic assessment is necessary to verify that the assumed savings can significantly outweigh this cost, resulting in a net benefit. This study provides the framework for conducting this economic assessment of implementing this system. The framework will act as a guideline to conduct a thorough economic assessment of various similar sustainable design concepts for sustainable cities.

Greywater accounts for most of the wastewater generated in a residence, making it an excellent resource for wastewater reuse, especially in this age of water scarcity. Fortunately, a household greywater system can make the best application of this potential by reusing the generated greywater. Although it may appear to be a convenient solution, it is necessary first to examine the economic feasibility of the system. This is because they demand a significant initial investment and frequent maintenance and replacement schemes. This study examines the economic impact of installing one of the greywater systems currently available in the US market, in a typical Houston residence. The life cycle costs (LCC) comparison shows a reduction in the overall utility bill and economic indicators such as net savings (NS), SIR or AIRR further justifies that it is an economically beneficial solution to be considered for the long term.

Widespread use of human and veterinary antibiotics has drawn attention to the occurrence and fate of antibiotics in the environment. Despite very low concentrations of these chemicals ranging from < 1 ng/L to hundreds µg/L, they are bioaccumulating in the environment and potentially spreading antibiotic resistance genes into ecosystems. The focus of current research interest is on municipal wastewater treatment plant (MWTP) effluents which are the main point sources for introducing these chemicals into surface waters. This study investigated the feasibility of using MWTP activated sludge as a cost-effective and sustainable adsorbent for sulfamethoxazole (SMX) removal. The activated sludge was collected from the aerobic basin of a biological nutrient removal (BNR) MWTP system. The sludge was oven-dried and used as an adsorption material without further modification. Langmuir and Freundlich isotherms were used to clarify the SMX adsorption parameters on dried activated sludge (DAS). The specific surface area of DAS correlates with the adsorption capacity. The specific surface area is reported as 10.5 m2/g for DAS. The data reported a rise in SMX adsorbed, from 0.02 to 11.07 mg/L, with an increase in the SMX initial concentration, increasing the interaction with adsorbent. SMX adsorption on DAS satisfactorily fits with the Freundlich isotherm, with n = 1.87, which implies a high adsorption affinity of SMX on DAS. Further modification could improve the adsorption capacity by adding functional groups with higher adsorption capacity of DAS toward pharmaceutical removal from water and wastewater matrices.

It is known that arsenic is remarkably toxic and can be found in high concentrations in a variety of natural waters and wastewaters both in Canada and worldwide. Thus, it is important to adequately treat these waters, with removal using adsorption methods being promising due to their simplicity, effectiveness, and relatively low costs. Agricultural residues are abundant in Saskatchewan, Canada, and can be utilized as an inexpensive biomass to produce adsorbents. In this study, biochar prepared from a raw canola straw biomass was investigated for its arsenate, As(V), and arsenite, As(III) adsorption capacities after being modified using phosphoric acid (H3PO4) and sodium hydroxide (NaOH). The biomass was treated prior to being converted into biochar using a prepyrolysis method. Canola straw biochar (CSB) was made through both conventional and microwave pyrolysis methods and a variety of different pretreatment parameters were explored including varying H3PO4 and NaOH to biomass ratios. Studying the effect of acid or base to biomass ratio suggested that the highest H3PO4 and NaOH ratio led to higher adsorption for both As(III) (10.59 µg/g and 12.46 µg/g, respectively) and As(V) (12.65 µg/g and 16.69 µg/g, respectively). Furthermore, varying solution pH at values of 3, 7, and 10 demonstrated that H3PO4 and NaOH CSB had markedly increased adsorption capacity at pH 7 for As(V). Overall, H3PO4 and NaOH CSB showed marginal adsorption enhancements for As(III) and As(V).

Agricultural and livestock run off are major sources of contamination of surface and ground waters by nitrogenous compounds, resulting in eutrophication and disruption of the ecosystem in these water bodies. Ammonium fertilizers and urea can be transformed into nitrate, which is highly soluble in water and can leach out from the soils and cause water pollution. Nitrate and nitrite have been known to cause health issues and thus should be prevented from entering water resources. The present study evaluated the effectiveness of pilot-scale permeable bio-barriers for the removal of nitrate, nitrite, and a mixture of nitrate and nitrite from contaminated water. The designed bio-barriers that are operated in continuous flow mode are comprised of parallel channels that can be run independently. Each channel has a bio-reactive compartment that houses an inexpensive carbon source and the biofilm. As part of this study, following the development of biofilm in the bio-reactive compartment, bio-barrier channels were fed with an aqueous medium containing either nitrite, nitrate, or both, to assess the impacts of nitrogenous compounds concentrations (10–250 mg L−1) and the presence of manure slurry supernatant on the removal of nitrogenous compounds at an average hydraulic retention time of 20 days. Apart from evaluating the performance of the bio-barrier in removal of nitrogenous compounds, composition of microbial community developed under various conditions has been examined. Results obtained to date show the effectiveness of engineered bio-barriers and their potential use as a solution for removing nitrite and nitrate from agricultural and livestock run off, hence preventing the contamination of ground waters by these compounds.

Lake Johanne, a shallow mesotrophic lake located in the Sainte-Anne-des-Lacs municipality in Quebec, is receiving water from a wetland discharge. This wetland water could be possibly contributing to this lake’s ageing. Thus, as an initial investigation, an on-site potential remediation of this wetland water discharge using a non-woven geotextile filtration technique is proposed. The method was based on a tank located near the lakeshore with a floating geotextile filtration system in continuous mode (i.e., 0.5-day retention time). On deployment, wetland water was pumped into the tank using a calibrated peristaltic pump (inlet), filtered by a selected set of non-woven geotextiles with distinct apparent opening sizes, and then returned to the lake (outlet) by gravity. This experiment ran throughout the summer and mid-fall of 2021. Samples from both the inlet and tank were taken every 2 days, and filter layers were changed upon clogging. The objective of this study was to improve the quality of this discharge before entering the lake by filtration. The water quality parameters monitored during the experiment from both inlet and tank samples were total phosphorus, total nitrogen, nitrate, chemical oxygen demand, total suspended solids, particle size, and turbidity. The non-woven geotextile filters removed nutrients, organic matter, and suspended particles at levels of 32%, 17%, and 29% for total phosphorus, COD, and turbidity removals, respectively, when the tank inlet and outlet were compared during the entire experiment. In addition, it was proven that the proposed method is easy to install, deploy, and quickly adapt to water quality changes. Even though the results have pointed out that additional investigation is needed, the treatment improved the quality of a portion of this wetland water. Thus, the feasibility of treatment has been shown for this possible remediation.

Extreme rainfall intensity–duration–frequency (IDF) relations are commonly used for estimating the design storm for the design of various urban hydraulic structures. Traditionally, these IDF relations were obtained by fitting the two-parameter Gumbel distribution to the annual maximum (AM) rainfalls for each rainfall duration independently using the method of moments (MOM). However, it has been widely known that this Gumbel/MOM-based traditional approach may not produce accurate estimates of extreme rainfalls as compared to those given by, for instance, the generalized extreme value (GEV)/L-Moment method as suggested in some recent studies. Consequently, there are several new IDF estimation procedures and products that are recently developed in Canada in an attempt to provide some improvements in the estimation of design rainfalls. This study proposed therefore new approaches for developing IDF relations based on the scale-invariance behaviour of extreme rainfall processes using the GEV distribution. A detailed comparative study was then carried out to compare the performance of traditional IDF estimation methods and the proposed new approaches using the available IDF data from 39 stations located across Canada with at least 50 years of record. Results of this comparative study have indicated that the new scale-invariance GEV approaches can provide the most accurate and most robust estimates of design rainfalls for all locations in Canada as compared to existing traditional methods.

Determination of suitable sites for small hydropower projects could offer new opportunities for sustainable developments. However, the non-scalable initial investigation costs are one of the biggest burdens when planning small projects. Moreover, solving a complex problem with only a few available parameters is almost impossible with many traditional models, and lack of data may make many design studies infeasible for remote, hard to access or developing areas. Artificial neural networks (ANNs) could help reduce investigation costs and make many projects feasible to study by acting as input–output mapping algorithms. This study provides an easy to understand and implement method to develop fast ANN-based estimation models using the multilayer perceptron (MLP) neural network and extended Kalman filter (EKF) or gradient descent (GD) as the training algorithm. Also, three approaches to feeding training data to the models were studied. Estimating runoff is an important challenge in water resources engineering, especially for development and operation plans. Therefore, the proposed method is applied for a runoff estimating problem using only easily measured precipitation and temperature. Results of this case study indicate that for a relatively similar performance, ANN models using EKF required a fewer number of neurons and training epochs than GD. Compared to the prior research in this study area, the methods in this study are much easier to understand and implement and are not dependent on data mining techniques or continuous long-term time series. Based on the results, a combination of the proposed data feeding methods and the EKF training algorithm improved estimation models by reducing the number of training epochs and the size of the network.

The global plastic waste generation increased drastically during the past decade, and therefore, microplastic (MP) input to the aquatic environment is growing exponentially. MP distribution in the aquatic environment is linked to particles’ physical characteristics. However, due to different origins and exposure to various weathering processes, MPs possess an extensive range of densities, shapes, and sizes. Turbulent structure induced by different mechanisms, such as temperature gradient, wind, and sudden topography changes, is another dominant factor affecting MP transport and distribution. This study aims to investigate the combined effect of size and density on the entrainment and mixing behaviour of MPs in a turbulent flow induced over the backward facing step. We used well-known sediment analogy criteria to study MP mixing and entrainment. Settling parameter, Stokes number, and trapping radius are used in this study to understand the entrainment and distribution of MPs in turbulent structures with different turbulent intensities. The first parameter describes the particle entrainment in the ambient fluid, while the other two parameters describe the distribution and mixing behaviour of particles. Our analysis demonstrates universal trends for MPs entrainment and distribution with the ambient flow dominated by MP size and density, combined with the turbulent flow hydrodynamics.

Canadian cities broadly promote green roofs as a sustainable solution that mitigates urban flooding and combined sewage overflows. This study assessed the performance of eight extensive green roof testbeds for rainwater retention, discharge intensity, and vegetation growth in a highly urbanized area of Toronto, Canada, over a complete growing season (May–October 2021). The 3.24 m2 testbeds were constructed with high-organic substrate and two planting types: (1) a mix of native forbs and grasses planted from seed and (2) commercial Sedum spp. mats. The two vegetation treatments have very different establishment times and, thus, illustrated vegetation cover’s role in green roof hydrology. The substrate in four testbeds was amended with 5.4% (v/v) biochar made from pyrolysis of sugar-maple sawdust. Discharge from each testbed was measured with a 0.2 mm tipping bucket, and vegetation cover was assessed monthly using a three-dimensional pin-frame. Subject to 90 natural rain events, the green roof testbeds completely retained 87% and 40% of small (0.2–4.8 mm) and medium (5–19.8 mm) rain events, respectively. Testbeds with Sedum spp. had higher retention than those planted with native plant spp. likely due to higher vegetated coverage. Biochar had positive effects on water retention and peak discharge for testbeds with native plants for small events and the vegetation growth of these plant species.

The Intergovernmental Panel on Climate Change has concluded that the frequency and intensity of extremes of heat waves, precipitation, droughts, and cyclones will continue to increase with every additional increment of global warming. Time-dependent changes in intensity and frequency of weather extremes require the non-stationary stochastic modelling of various environmental loads in the structural reliability assessment of infrastructure systems. Since most of the existing design codes and standards assume ‘stationary climate’ conditions, the effects of the transition to non-stationary conditions on infrastructure risk and reliability assessment must be carefully investigated. Whether the current design philosophies will ensure proper safety for our structures under these changing conditions, is a matter of concern from the engineering perspective. Therefore, this paper develops a non-stationary Poisson process model of extreme rain events to support the assessment of the increased risk of flooding in the future. The proposed model has a baseline model of precipitation events that are based on historical data available from existing weather stations. Then, time-dependent amplification functions, based on the findings of climate models, are assigned to the frequency and intensity of rain events. To illustrate the proposed approach, the paper analyses precipitation data from Toronto International Airport Station. The results indicate that the return periods of the extreme one-day rainfall events in Toronto corresponding to higher threshold magnitudes decrease significantly over time. The distribution of the extreme values of one-day rainfall events increases considerably with increase in design life of the structures.

The government of Newfoundland and Labrador (NL) provides climate projections for a range of important infrastructure design parameters. A partnership between Memorial University and the Government of NL, funded by Natural Resources Canada, aimed to train engineers and planners on how to use these data for infrastructure planning and design. After several workshops, it became clear that a significant obstacle to implementing some of these measures existed due to differing perceptions of uncertainty between stakeholders at the local level and technical experts. Effective communication between technical personnel and stakeholders must make explicit the meaning of “uncertainty” with which they are working for decisions to be made under uncertainty. Uncertainty in the sense of surprise suggests that it is important not to be faced with an event that was not known to be possible. This represents a risk perspective where uncertainty refers to known outcomes with unknown probabilities that is used by technical experts. There are robust methods to deal with risk from this scientific perspective, but decision makers at the local level feel unable to deal with this kind of uncertainty. A vulnerability perspective of uncertainty, with the sense of ignorance, suggests an aversion to being mistaken, particularly by an error that leads to a negative outcome, which is the meaning with which many stakeholders tend to work. This includes risk and probability, and also allows for ambiguity and vagueness that are qualitative sources of uncertainty. Clarifying these two senses allows for aleatory and epistemic sources to be more clearly defined and dealing with natural and model uncertainty can be put in perspective. The conversation can be shifted from the technical/engineering/scientific aspects of uncertainty to issues under the control of local stakeholders and decision makers. This shift facilitates the inclusion of local knowledge and greatly increases the chance of sustainable actions through a robust process of decision making.

The worldwide exponential upsurge of photovoltaic panel installations and the subsequent heights of photovoltaic waste is a matter of intense concern. There is an estimation that within next 2050 year, the worldwide generation of photovoltaic waste may rise upto 60–78 million tons. The objective of this study is to identify the crucial barriers to photovoltaic solar waste management in Canada and prioritize them. At first, the barriers to photovoltaic solar waste management were identified through literature review and expert feedback. Face-to-face interviews were conducted with the selected seven experts who have comprehensive knowledge and expertise on solar management in Saskatchewan. In this study, Analytic Hierarchy Process (AHP) is used to analyze and find prioritization among these barriers. Some crucial barriers from each category are lack of legislative framework, lack of monitoring and supervision, generation of the low volume of solar waste, low profitability in recycling, lack of consumer awareness, and lack of knowledge about business opportunities. Among the rest, lack of restriction on landfill disposal, undefined role of stakeholders, lack of subsidy and tax rebate, additional cost for consumers, lack of knowledge about business opportunities, insufficient campaigns are also worth mentionable barriers. This study is expected to contribute to the concerned government agencies to assess, evaluate, and utilize the priority of barriers to establish a sustainable and resilient solar waste management plan in Saskatchewan, Canada.

With the development of the renewable energy sector, solar energy resources are playing a crucial role. Worldwide the existing solar electricity generation is over 450 GW and it is predicted to increase to 5500 GW by 2050. In this study, the forecasting of electricity by photovoltaic solar in Canada has been performed using historical data and regression analysis. Three regression analysis methods (linear regression, polynomial regression, and power regression) are used in this study. After that, a comparative analysis was performed with the estimation of volumes made by the Canada Energy Regulator with an estimation of volumes obtained by all three regression models. The findings of the study show that the polynomial regression model is the best model having the highest R2 value (0.9359). In contrast, the forecasting trend of volumes indicates that it is matching more with power regression than polynomial regression after the year 2030, whereas it followed the linear regression in the first decade. Following the functional pattern of historical data, the volume is forecasted to be 8646 MW by linear method, 13,486 MW by polynomial method, 28,864 MW by power method in comparison with 23,439 MW by Canada Energy Regulator in the year 2045. The result of the study can be used by government policymakers, other research authorities, or scholars. On the other hand, in practical life, the analysis of comparison of forecasting of electricity can be used for forecasting of electricity from different alternative sources like biomass, wind, hydro, and others.

The Meadoway project, led by the Toronto and Region Conservation Authority (TRCA), aims to restore 200 ha of meadow habitats and complete a 16-km linear multi-use trail along the Gatineau Hydro Corridor across Scarborough, Ontario. This ambitious project is demonstrating that urban ecosystem restoration can be successfully implemented at a large city-level scale. The hydro corridor’s transition from turf grass to deep-rooted native meadow plants is hypothesized to enhance the regulating (e.g., erosion and flood control) and supporting (e.g., soil quality regulation and nutrient cycling) services. This study aims to evaluate this hypothesis. In-situ infiltration, penetrometer tests, and soil sampling were conducted on two pre-restored turf lands and two restored meadows in 2020. Soil samples were analyzed for bulk density, porosity, total carbon, total nitrogen, and available phosphorus. Water balance analysis was conducted by simulating artificial rainfall events with different return periods upon undisturbed vegetated soil samples with accompanying saturated hydraulic conductivity measurements using a mini-disk infiltrometer. The restored meadows had a similar in-situ saturated hydraulic conductivity with turf lands, but had a lower cone index, and a higher bulk density and lower porosity than the turf lands. The soil nutrient content showed large variation among different sites. However, a notable trend in soil available phosphorus was measured where it was consistently higher in surrounding pre-restored turf lands than in restored meadows. The rainfall simulation tests showed that the turf lands generated much more surface runoff than the restored meadows during rainfall events with return periods from 2 to 100 years, while the saturated hydraulic conductivities of soil samples from the restored meadows and the turf lands were not considerably different. This indicated that the saturated hydraulic conductivity measurement is not a good indicator to quantify the hydrological regulating functions of green infrastructure like The Meadoway.

Climate change adaptation planning and solutions for coastal infrastructure and navigation in the St. Lawrence marine corridor, which plays a key role in Canada’s economy and supply chain, are highly dependent on the availability of climate change information at high spatial and temporal resolutions. In this study, ultra-high-resolution regional climate model simulations are implemented using Environment and Climate Change Canada’s Global Environmental Multiscale (GEM) model for current and future climates. Advanced and targeted diagnostics are used to identify vulnerability hotspots and opportunities to address specific climate risks within the corridor. First, an ultra-high spatial resolution (~ 4 km) simulation spanning the 1989–2010 period for a domain covering the St. Lawrence marine corridor is performed using the GEM model driven by the ERA5 reanalysis. Comparisons of modelled climate fields and parameters relevant to infrastructure and navigation with available observations confirmed the ability of the model to simulate important processes, mechanisms, and seasonality. This is followed by future climate simulations, spanning the 2041–2060 and 2081–2100 periods for Representative Concentration Pathway 8.5 scenario, driven by Canadian Earth System Model (CanESM2) outputs. Given the coarse resolution of CanESM2, a grid-telescoping approach is used, i.e. a 10 km spatial resolution GEM simulation driven by CanESM2 is first performed, the outputs of which are used as lateral boundary conditions for high-resolution GEM simulations at 4 km horizontal resolution. Advanced diagnostics focused on extreme weather and climate are used to understand and pinpoint potential climate risks within the St. Lawrence marine corridor, particularly with respect to navigability, and the potential climate resiliency of key transportation assets in the study region. This paper will present these results, which will form the basis for additional detailed investigations on climate-infrastructure interactions and other climate resiliency studies.

Drinking water system (DWS) is an important public infrastructure that plays a significant role in attainment of a healthy lifestyle. To ensure that a DWS is functioning efficiently, the first step is to know the existing condition of a water system. A complete understanding of DWS is only possible when its current performance is both measured and compared against the similar water systems. Performance assessment becomes much more critical specifically in small and medium-sized water systems (SMWSs) because these systems constantly grapple with various constraints, including but are not limited to insufficient fund, limited staff, inadequate infrastructure, and absence of advanced water treatment. A questionnaire was distributed to the municipalities across the Okanagan Valley to identify the key performance indicators (KPIs). Thirty-nine KPIs were identified across six performance criteria: environment, finance, infrastructure, staff, and operation and monitoring, and social and institutional. Municipal representatives rated these KPIs based on relevancy, clarity, reliability, and comparability. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multicriteria decision-making method was employed to calculate the performance index. Analytical Hierarchical Process was used to calculate weights of performance criteria, whereas KPIs aggregation was based on an Ordered Weighted Average technique. In this study, performance of three small and medium-sized municipal water systems is measured. Necessary data corresponding to each criterion for all the KPIs were also collected. The results show that performance index of water system “X” was found to be the highest (62), followed by Y (33) and Z (21). Water system “X” had the highest aggregated score for operation and monitoring criterion, while “Y” and “Z” had the same score for environment criterion. The findings from the study indicated key areas that require municipal interventions to enhance drinking water system performance and ensure high quality life.

Research was conducted on non-agriculture source material (NASM) data and legislation to identify knowledge gaps and limitations associated with the regulatory framework within Ontario. The framework for NASM land application on farm fields is set out in Ontario Regulation 267/03 under the (Nutrient Management Act, 2002, S. O. 2002, Chap. 4. https://www.ontario.ca/laws/statute/02n04 ). Currently, Category 2 NASM (including waste and wash-water from processed fruits and vegetables) and Category 3 NASM (including dairies, abattoirs, and sewage biosolid facilities) have stringent requirements that agri-food processors must follow when looking into land application disposal. Agri-food processors have suggested that the regulations are overly burdensome for smaller facilities due to the cost for ongoing sampling, documentation, and reporting requirements, which hinders their economic expansion. Following the requirements set out in Ontario Regulation 267/03, agri-food processors have identified that their food processing wash-water samples rarely exceed the regulatory limits for heavy metals under Ontario Regulation 267/03. Thus, processors are questioning why they need to continue analyzing for these constituents as this is a repetitive cost to their business, and if any improvements can be made to the regulatory framework to make it more economically feasible without harming the environment. Overall, this project looked at identifying possible improvements to the Ontario Regulation 267/03 framework to assist agri-food processors with cost-effective land application, with a focus on heavy metal sampling requirements. In the first phase of the project, data was collected and summarized from wash-water facilities to characterize the NASM they produced. The NASM data from various agri-food facilities showed that most of the samples were routinely below the lowest threshold for concentration (CM1 level) and always below CM2 levels, a higher concentration but still within the allowable regulatory limits. Next, a thorough jurisdiction scan of North American standards gave insight into possible improvements to Ontario's land application framework. Findings show that Ontario has established one of the best systems for land application to protect the public and the environment. However, it is evident that this regulatory framework is overly burdensome for non-variable food processing sources and hinders the feasibility of diverting this beneficial organic matter in the form of NASM from landfills. Recommended improvements include decreased sampling frequencies for heavy metals if there is an evidence of low metal occurrence and increased application timeframes if the weather permits.

With ever-growing plastic production, the pollution of microplastics (MPs) has become a threatening environmental problem in the twenty-first century. It is crucial to investigate MPs’ hydrodynamics due to their widespread pollution in aquatic environments. MPs are particularly difficult to characterize because they not only have a wide range of sizes and densities and are also found with highly variable shapes. So far, no numerical investigation exists that has sufficiently accounted for MPs’ complex shapes. To accurately predict the fate, transport, and mobility of aquatic MPs, formulating settling hydrodynamics of MPs with complex shapes is essential. In the present study, a finite volume-based three-dimensional numerical model is employed to investigate the settling trajectories of MP particles with a wide variety of shapes and densities in a quiescent fluid. Seven test runs are performed utilizing the present model, with negatively buoyant MP particles whose densities range from 1100 to 2000 kg/m3. The particle Reynolds numbers of these settling particles fall in the nonlinear range of 100–1300. Our initial results have proven the present model’s robustness in simulating the dynamics of MP-sized particles in a quasi-static fluid. Preliminary results with MP particles of regular shapes are shown to be consistent with previous empirical formulations derived from particle settling experiments. With the present model, complex shaped MPs, e.g., thin cylinders, resembling MP films, are also tested. The settling dynamics of thin cylindrical MP particles obtained from the present model match well with the settling patterns observed from experiments. Results from the present model will be used to parameterize MP particles with irregular shapes, enabling the prediction of MPs’ transport history through large-scale models.

In the face of urbanization and climate change, Canadian municipalities have intended to supplement the centralized drainage system with decentralized stormwater management practices, such as bioretention systems, to better management urban stormwater runoff. However, it is not uncommon to observe that bioretention systems leach nutrients (both phosphorus (P) and nitrogen (N)), particularly in the beginning of their operation. This study examined the effects of six amendments selected primarily for reducing P leaching of bioretention systems through monitoring six amendment and two control cells in the field in the growing season of 2020. In addition, two amendments were examined in the laboratory setting to investigate the influence of amendment mixture percentage on their performance using laboratory columns. The field results showed that all amendments had the capability of preventing or mitigating P leaching from bioretention systems, with the water treatment residual (WTR) outperforming all other amendments, followed by the sorptiveMEDIA (SM) and activated aluminum (AA). In addition, some of the amendments (i.e., drywall (DRY), WTR, and SM) were also found to be beneficial in reducing the N leaching to a slight degree, whereas eggshell (EGG) introduced an extra source of N leached. Furthermore, the temporal evolution of the P leaching of the amendment cells was found to be different from that of the control cells, whereas same result was not observed in the N leaching. The laboratory results further confirmed the effectiveness of SM and AA in reducing P leaching. The results also revealed that the increase of the mixture percentage of amendment did not always improve the performance (in term of P removal rate) and reduce the degree of P leaching. The observed difference in the P leaching behavior between the bioretention cells and columns suggests that caution should be paid when translating laboratory knowledge into the field.

The design and construction of reinforced concrete structures in the arctic is of increased interest. Concrete and steel properties change as temperatures drop below freezing. Change in concrete properties such as compressive and tensile strengths and elastic modulus can be large. The changes in steel properties are smaller over this temperature range, but could still be important. These changes in properties due to arctic temperatures will impact the structural behavior of reinforced concrete elements as well as frames. In this study, representative elements of reinforced concrete frames are analyzed to determine how changes in element stiffness over a range of temperatures influences moment demand in frame elements based on variations in material properties with temperature that are reported in the literature. The temperature-affected strengths and stiffness of these elements were incorporated into a simple, analytical frame model to assess the impact of reduced temperature on frame behavior. In particular, the changes in demand on elements and connections for service load conditions as the temperature decreases are evaluated.

In this study, an effective and low-cost water treatment system is developed. A critical water contamination problem, experienced by the rural, remote, First Nation communities was identified, which is Manganese. Then a treatment system was developed to effectively remove Manganese from the source water. Greensand plus was used as a filtration media, and the prototype was designed to best suit the need of the community residents to remove Manganese in raw water. The laboratory scale experiments were designed using Design Expert software; results were analyzed using RSM method, and the test apparatus was able to reach a removal efficiency 96.50% which can effectively treat source water levels found in the raw water sample. Therefore, a fit-for-purpose solution is developed to remove Manganese from raw water, which is cost-effective, easy to use, and maintain.

Aerobic granules, with a size range of 0.2–5 mm, are a dense colony of different strains of microorganisms held together by extracellular polymeric substances (EPS) secreted by the bacterial cells under special operational conditions. These granules are the principal components of the aerobic granular sludge (AGS) biotechnology. AGS has been widely and successfully applied for the treatment of municipal and different industrial wastewater streams in the past two decades and has the potential for successful application in cold regions. Moreover, in addition to efficient wastewater treatment, AGS also offers great opportunity for high-value resource recovery. AGS granules contain high phosphorus and high EPS contents. The high EPS content presents an opportunity to recover numerous high-value resources including xanthan and curdlan from AGS biosolids. Xanthan is widely used in the food, biomedical, and oil industries, as well as for soil strengthening in geotechnical applications. Curdlan is used in biomedical and pharmaceutical, food, cosmetic, as well as construction industries. These resources are currently in high demand globally and research efforts geared towards their recovery would be imperative to achieving the biorefinery concept in wastewater. Literature search shows that research is needed on the optimization of the biosynthesis of xanthan and curdlan in AGS bioreactors while maintaining the wastewater treatment capability of the granules. In addition, the market potentials of xanthan and curdlan needs to be determined in order to make their recovery from waste aerobic granules lucrative. This paper provides an overview of resources recoverable from waste aerobic granules.

Oil spills are a major concern in oceans. Chemical dispersants are widely used as effective oil spill treating agents in the marine environment, especially in cold regions. Microplastics (MPs) are widely observed in oil-polluted oceans. Understanding how MPs affect oil dispersion thus becomes essential. Recent studies presented the negative influence of MPs on oil dispersion effectiveness due to the existence of MP-oil-dispersant agglomerates (MODAs). However, it is still unclear how MPs with various particle sizes would interact with oils during dispersion using different dispersant-to-oil volumetric ratios (DORs). Our study explored the effects of MPs with five sizes on oil dispersion under two DORs. Polyethylene (PE) MPs (7, 13, 23, 40, 90 μm) were applied. Newfoundland offshore and low sulfur crude oils were used. Corexit EC9500A was selected as the chemical dispersant. Results indicated that MPs affected oil dispersion effectiveness under various conditions owing to MODA resurfacing. The dispersion effectiveness of low sulfur oil at DOR 1:100 was between 21.43 ± 3.72 and 35.47 ± 3.06%, with MP size rising. With Newfoundland offshore oil, the dispersion effectiveness was greater than Low Sulfur oil. It increased obviously from 57.62 ± 3.46 to 88.42 ± 14.23% under DOR 1:100 with MP size rising. The oil droplet size of Newfoundland offshore was smaller than that of low sulfur under both DORs. Findings from this research will provide fundamental data for future decision-making on oil spill response in cold regions in the presence of MPs.