Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 3

Table of Contents

1. A Framework for Integrating Virtual Reality into Job Hazard Analysis in Construction

   Nikhil Uday Gurav, Zia Ud Din, Ahmed Senouci, Jack Toellner, Walid Maherzi

   Abstract

   Job hazard analysis (JHA) is crucial for safety risk assessment and control in the construction industry. However, workers using traditional JHA methods that lack visualization tools may not be able to identify all potential hazards. This research proposes a framework for implementing virtual reality (VR) in JHA. The study employed a two-fold approach: first, examining current JHA procedures through site visits and interviews with construction professionals, and second, implementing a VR-based JHA. Two construction experts tested the proposed VR-based JHA and provided feedback on its feasibility and user experience. The experts feedback revealed that immersive VR environments can potentially enhance hazard detection for new workers. However, practical challenges, such as access to Wi-Fi, power supply, hardware availability, integration with existing systems, and user acceptance, must be addressed for successful VR integration into JHA. This study demonstrates VR’s potential to enhance construction safety while highlighting the challenges that must be overcome to realize its full benefits in improving workplace safety and proactive hazard mitigation.

2. Optimizing Construction Project Scheduling for Improved Effectiveness and Reduced Labor Costs

   Berlant Arab, Wahbi Albasyouni, Mohamed Elsaid, Yasmeen Essawy, Khaled Nassar

   Abstract

   The construction project schedule plays a vital role for project managers in the construction industry, enabling effective control over project timelines, expenses, and quality. Developing project schedules is often a challenging task, heavily relying on the experience of project planners and various project schedule elements such as resource allocation, duration, and cost. However, the optimization of resources in construction project scheduling, particularly for infrastructure projects, remains a significant challenge. This research addresses the need to enhance the schedules with the optimum resources in the construction industry while simultaneously aiming to decrease labor costs. The proposed approach depends on resources optimization methodology, specifically tailored to enhance project scheduling for improved resource utilization and reduced project duration. An essential factor in developing a practical construction schedule is having a deep understanding of the project’s conditions and its activities. The suggested model comprises three essential phases: extraction, configuration, and simulation. In the extraction phase, detailed information about the project’s activities and relevant parameters is obtained. Subsequently, in the configuration phase, relevant parameters are incorporated into the model as decision variables, enabling the generation of different scenarios. Finally, the simulation phase rigorously evaluates these scheduling scenarios, aiming to achieve plans that not only reduce project duration but also significantly lower labor costs. By focusing on these three phases, the model aims to improve the efficiency and effectiveness of construction project scheduling. Considering the complex relationships between activities in various construction projects, the main goal is to achieve optimal schedules that find a balance between resources utilization and labor costs. Lastly, this research endeavors to contribute to more streamlined and reliable processes for construction projects.

3. Transforming Parking Facilities for Electric Vehicles: An Optimal Design

   Kimia Khalili, Po-Han Chen, Fuzhan Nasiri

   Abstract

   Electric vehicle (EV) adoption is growing rapidly across Canada and around the world. The federal government has committed to achieving 100% zero-emission car sales by 2035; however, challenges persist, particularly in the form of limited charging infrastructure and the resulting frustration among EV users. This paper addresses the issue with inadequate charging stations by emphasizing the significance of accurate prediction and strategic planning for meeting future charging station needs. In particular, this research provides an analysis for estimation of the number of required charging stations and aims at identifying optimum location selection strategies for EV charging stations in commercial buildings in Quebec. The research adopts an owner-centric approach, recognizing the importance of considering owners needs and frustrations in the planning process. The selection of the charging station locations is guided through minimization of the life cycle costs associated with EV charging infrastructure from an owner's perspective. In doing so, the proposed EV-MathOpt model incorporates factors such as equipment, operation, and maintenance costs. The findings of this study are expected to contribute valuable insights that not only address the immediate challenges faced by EV users but also align with broader goals of promoting sustainable transportation and meeting government mandates. In conclusion, this research serves as a critical step toward fostering a seamless and cost-effective transition to an electrified future in Quebec's and in broader spectrum North America commercial buildings.

4. Enhancing Construction Site Safety and Efficiency with YOLO v8-Based Computer Vision Model

   Mohamed Sabek, Vicente Gonzalez, Qipei Mei, Gaang Lee

   Abstract

   The construction industry, which is characterized by complex operations and dynamic environments, creates many safety hazards that demand continuous innovation for enhanced safety and efficiency. Traditional approaches to monitoring construction sites, which are often manual and error-prone, contribute to safety hazards and operational inefficiencies because they are usually manual operations. To address this, our research introduces the innovative use of the state-of-the-art You Only Look Once (YOLO) v8 architecture to develop a computer vision model specifically trained for the detection of construction vehicles with real-time detection and tracking abilities running on any low-wattage CPU. YOLO is a fast object detection architecture that treats detection as a single regression problem to predict bounding boxes around objects in images, which enables it to perform significantly faster than region-proposal-based methods. This study aims to address the limitations of traditional monitoring systems used at construction sites, particularly regarding real-time vehicle detection and tracking. It identifies a gap in the existing construction-site monitoring systems that lack AI integration, resulting in operational inefficiency and low safety risk detection. However, most AI models that support computer-vision-based operation monitoring require high-power processors, which are challenging to deploy in traditional monitoring systems. Although the recently introduced YOLO v8s has the potential to work with relatively low-power processors, its potential in construction operation monitoring has not yet been explored. Therefore, the primary objective of this study is to train a robust and accurate YOLO v8s model that can function effectively under diverse and challenging conditions at construction sites. Our methodology encompasses the comprehensive data collection of various construction vehicle images and rigorous model training and optimization to ensure high accuracy and real-time processing capability optimized by OpenVino from Intel. The findings reveal that our optimized YOLO v8-based model significantly outperforms traditional manual monitoring methods that do not have AI monitoring capabilities while demonstrating high accuracy in real-time vehicle detection and tracking. This research catalyzes the integration of advanced computer vision technology into construction management to enhance on-site safety and operational efficiency, contributing to its adoption in the field.

5. Practicality of Implementing Sub-clause 8.4 [Advance Warning] of the FIDIC 2017 Red Book in Egypt

   Gehad Ibrahim, Waleed El-Nemr, Ahmed Samer Ezeldin

   Abstract

   Disputes in construction projects can cause cost and time overruns that can be detrimental to the parties involved and to the project. Several measures have been advocated by the industry in an attempt to avoid disputes and their adverse impacts. One of these measures is the introduction of advance warning sub-clauses in construction contracts, where the contracting parties play a proactive and collaborative role in warning each other of potential adverse impacts to the project. Such clauses have been in the NEC suite of standard construction contracts for decades and have recently found their way to the 2017 edition of the FIDIC Red Book. In light of the consistent popularity of the FIDIC contract in the Egyptian construction industry for nearly three decades, this study explores the practical and legal challenges of implementing sub-clause 8.4 [Advance Warning] of the 2017 edition of the FIDIC Red Book in the Egyptian construction market. Through carefully crafted scenarios and interview questions that are derived from an extensive literature review on the topic, interviews are conducted with practitioners in the industry to explore the extent of the market’s willingness to adhere to the obligations set forth in sub-clause 8.4.

6. Exploring the Capability of Earned Value Management in Construction: A Comparative Analysis

   Mohamed Elsaid, Khaled Nassar, Ibrahim Abotaleb

   Abstract

   Earned value management serves as a crucial and multi-dimensional approach to project control, integrating both time and cost dimensions. Studies have emphasized the significance of time schedules as a key factor in project success. However, challenges related to conceptual understanding and implementation difficulties have emerged. Additionally, concerns have been raised about the inherent limitations of EVM, particularly its emphasis on measuring the “amount of work performed” rather than time deviations. This study conducts a thorough analysis of two earned value methodologies Earned Duration and Earned Schedule, across a diverse dataset of construction projects in Egypt. The primary aim is to assess the precision of these methodologies in predicting project completion dates and identify the most effective approach based on project duration percentages. Findings emphasize the pivotal role of considering project type and duration when selecting the most accurate and efficient methodology, thereby advancing project management practices within the Egyptian construction sector. The implications of this study are pertinent to construction project managers, providing them with valuable insights for informed decision-making and enhanced precision in project forecasting. Given the evolving landscape of the construction industry, practical guidelines are introduced for choosing the most efficient methodology based on project duration percentages, offering project managers a structured framework for decision-making. Moreover, tailored recommendations related to performance factors such as SPI and SCI contribute additional insights to optimize project management practices in Egypt, positioning this analysis as a valuable resource for achieving superior outcomes in diverse construction projects.

7. Edge Computing for Real-Time Monitoring Systems in Construction Sites

   Amir Shahbazi Ojghaz, Farnaz Sadeghpour, Sayeh Bayat

   Abstract

   This paper presents an innovative approach to real-time monitoring in construction management by integrating Building Information Modeling (BIM) with edge computing to develop a hybrid system. The study explores how this proposed system addresses the critical demand for immediate data access and rapid response, overcoming the limitations of traditional standalone and client–server systems in dynamic construction environments. The research follows the development and performance assessment of a prototype through a case study focused on site safety monitoring. The prototype utilizes ultra-wideband (UWB) sensor technology for precise worker tracking and alerting within hazard zones, offering significant improvements in time latency compared to conventional systems. Results indicate that the hybrid system substantially reduces the response time to potential hazards, enhancing safety measures. The hybrid system's architecture also allows for standardized and flexible data management with BIM, facilitating the expansion of applications without complex modifications to server architecture. Through rigorous experimentation, the study demonstrates the hybrid system's superior performance with the ability to issue alerts more rapidly and reliably than client–server models. The research underscores the potential of this hybrid approach to advance real-time monitoring in construction, promising benefits for safety, efficiency, and management, while also acknowledging the limitations of hardware capabilities and real-world construction conditions.

8. Optimized Bridge Repair and Maintenance Plan Using the Markovian Model

   Samar Nadeem, Habiba Mahdy, Mohamed Zaki, Ossama Hosny, Hatem Elbehairy

   Abstract

   Infrastructure is essential for the functioning of modern societies and economies, thus investing in infrastructure can lead to economic growth, improved public safety and security, and an enhanced quality of life for people in each area. The aim of this study was to implement a 5-year plan with the objective of maximizing the overall benefit–cost ratio (BCR) of a network of bridges within a specific budget. This objective was achieved by utilizing two optimization models. The first model focused on optimizing the Markov Chain Transition Probability Matrix (TPM) to forecast the deterioration level of each bridge component. Based on this optimization, a 5-year plan was devised, considering a set yearly budget to achieve the optimal BCR. The results of the model showed an average BCR of 6.82, with the total repair cost for the 5-year plan falling below the allocated budget for the entire period. This indicates that the model successfully identified the best maintenance strategy for repairing various bridge components while adhering to predefined constraints that ensured each component’s condition did not fall below a rating of 4. These outcomes were achieved while staying within the predetermined annual budget, highlighting the success of the model in formulating a maintenance plan to address damaged bridge components and enhance the network’s overall BCR while upholding all constraints.

9. Implementing AI to Improve the Efficiency of BIM-Based Construction Schedules

   Larin Jaff, Gursans Guven

   Abstract

   In construction project management, it is crucial for the project’s success to keep track of on-site progress and to maintain construction schedules up-to-date. A lack of real-time schedule updates can result in delays in construction scheduling. Additionally, manual methods used for updating schedules can be time-consuming and often lead to inefficiencies in practice, especially with complex projects. This paper presents a novel approach integrating Artificial Intelligence (AI) and Building Information Modeling (BIM) that builds on the synergy between OpenAI’s Generative Pre-trained Transformer (GPT), and Autodesk Revit, a leading BIM authoring software. In this novel approach, a practical tool is developed that facilitates automatic updates to construction schedules using Natural Language Processing (NLP) within a BIM model. It allows field personnel to utilize their smartphones or tablets to verbally interact with and update the schedule of a project without direct access to its BIM model. This method streamlines the schedule updating process and enhances accessibility and convenience for on-site management. The AI and BIM integration demonstrated in this study represents an essential shift in construction project management, potentially leading to enhanced efficiency, time management, accessibility, and cost-effectiveness in projects. This paper provides an overview of this practical tool that works toward the integration of construction project scheduling and management into the digital age.

10. Circular Economy Solutions for Construction and Demolition Waste: A Comprehensive Review and Sustainable Perspectives

    Shiyao Zhu, Haibo Feng

    Abstract

    Construction and demolition waste (CDW) constitutes approximately 30% of global waste generation, posing both financial burdens on contractors and significant environmental hazards. Addressing this challenge, the adoption of a Circular Economy (CE) approach emerges as a promising strategy for sustainably managing the immense volumes of CDW. This study adopts a comprehensive literature review on exploring the circular solutions of CDW and their possible impacts. The methodological framework encompasses literature screening and a solid review scheme, culminating in the selection of 128 articles for integrative analysis. The resulting mapping revealed the time evolution of countries, publications and research topics. Nearly half of the research is from European countries and nine major topics have been identified. The topics are later grouped into three aspects for integrated analysis and discussion, which are circular solution, sustainability, and method and tools. Illustrations of relevant circular solutions are provided for the design, construction, end-of-life phases, as well as material circularity and production. Future research can explore several promising directions within the realms of wood waste circulation, social performance emphasis, stakeholder network building, and the development of new life cycle tools with respect to the above three aspects. This comprehensive analysis not only contributes valuable insights into the current state of CDW within the circular economy paradigm but also directs attention toward critical avenues for future research in this area.

11. Integrating Learning and Forgetting Rates: A Simulation Approach to Assess the Influence of Delay Risks on Project Scheduling in Construction Repetitive Projects

    Tayseir S. Hegazy, Yasmeen A. S. Essawy, Khaled Nassar, Khaled Hamdy, Hatem El-Behairy

    Abstract

    Scheduling repetitive construction projects presents significant challenges, necessitating the integration of various factors to enhance project scheduling realism. The learning rate, defining how quickly a project team absorbs knowledge and best practices, is a crucial element affecting the duration of such projects. This continuous learning and process improvement commitment faces difficulties in maintaining consistent workflow due to the stochastic nature of construction endeavors. Work stoppages, resulting from various events, disrupt workflow and hinder the accumulated learning rate, impacting project duration. Employer risk events (ERE) play a substantial role in causing interruptions, necessitating an assessment of their impact on the project’s learning rate. The study focuses on two aspects: quantifying learning loss from work stoppages for accurate project performance calculations and refining corrective measures to minimize repercussions on duration and cost. The complexity of considering learning and forgetting rates, coupled with uncertain events like ERE, calls for the essential use of simulation software in project scheduling. The method devised for quantifying learning loss proves valuable for claims analysis and corrective measure implementation, contributing to improved project management efficiency. Ultimately, the study aims to mitigate learning loss with minimal impact on project duration and cost, addressing the intricacies of scheduling repetitive construction projects effectively.

12. Comparative Analysis of Urban Road Networks Through Space Syntax Analysis

    Yasmeen A. S. Essawy, Ashraf A. Aguib, Khaled Nassar

    Abstract

    Space syntax, a powerful analytical framework rooted in architectural and urban design, has emerged as a valuable tool for unraveling the complex spatial configurations within various networks. By emphasizing the relationships and connectivity patterns embedded in physical space, space syntax offers a unique perspective that proves insightful in the analysis of diverse networks, ranging from urban road systems to social structures. By investigating the spatial relationships and connectivity patterns inherent in road systems, space syntax analysis offers valuable insights that can inform more effective and sustainable strategies for urban mobility and infrastructure design. This study presents a comparative study of two (2) road networks in different neighborhoods, employing space syntax analysis to identify common spatial patterns and variations. It, also, investigates the influence of space syntax on pedestrian movement within urban areas, and analyzes how the spatial configuration of road networks affects pedestrian accessibility, safety, and overall urban walkability. The findings not only contribute to advancing the understanding of urban road networks, but, also, inform evidence-based strategies for enhancing the practical applications of space syntax analysis, and informing sustainable and resilient urban development.

13. Advancing Understanding and Addressing Suicide Among Construction Workers in the US: An Exploration of Current Research and Future Directions

    Yuting Chen, Lingguang Song

    Abstract

    The escalating rate of workplace suicides in the US, particularly within the construction industry, underscores a critical need for comprehensive research and intervention strategies. Despite a notable increase in attention and collaborative efforts, including initiatives such as Construction Suicide Prevention Week, the awareness of risk factors among construction workers remains insufficient. This study aims to address the gaps in existing knowledge by examining the factors related to suicide among construction workers and proposing a theoretical framework integrating the interpersonal-psychological theory of suicide (IPTS) and psychache theory. For future work, the research emphasizes the need for empirical testing of the proposed framework, encouraging collaboration with industry stakeholders, mental health professionals, and relevant organizations.

14. A Maturity Model Defining the Synergy Between Digital Twin and Lean Construction to Support Construction Progress Monitoring

    Jorge Mauricio Ramirez Velasquez, Ivanka Iordanova

    Abstract

    Low productivity in the construction industry is a long-standing issue. Over the years, stakeholders have developed strategies to address it, resulting in the emergence of two types of approaches. The first is “managerial,” where the lean construction (LC) philosophy, with its principles and tools, is recognized as one of its most important contributors. LC focuses on increasing value for the client, reducing waste and improving the performance of management practices at each stage of the construction projects. The second approach is “technological,” aiming to facilitate the connection between the cyber and physical worlds. Within this second approach, the digital twin (DT) concept is highlighted as one of the most promising technologies bridging both the cyber and physical worlds. To contribute to the body of knowledge on construction progress monitoring, this article first argues the necessity of a synergistic connection between both LC and DT. Secondly, it proposes a maturity model (MM) to conceptualize such an integration. This study is structured as follows: In the first part, a brief introduction exposes the general considerations. The second part summarizes the literature review concerning the integration in the construction phase of some concepts and tools belonging to the managerial and technological approaches, and its associated challenges. In the third part, an analysis of the theoretical feasibility of the proposed synergy is developed, based on a synthesis of the literature review. In the fourth part, a maturity model describing the types of synergic functionalities between both LC and DT for construction progress monitoring purposes is presented and explained. Finally, in the last part, a discussion about the proposed synergy, along with future trends in this domain are presented.

15. Structure Repair & Strengthening Simulation and Optimization Model Incorporating RC Jacketing & Externally Bonded CFRP

    Athnasious Ghaly, Mahmoud Amin

    Abstract

    For the past few decades, the construction industry has become more dependent on management and simulation tools, for more accurate and efficient estimation of time, cost, and behavior of projects. Several research have been published in the construction and optimization modeling field; however, few to none have investigated the structure repair process with its different alternatives. For instance, reinforced concrete (RC) structure has been repaired and strengthened with RC Jacket for many decades, till a new system was introduced to the market in the early 90 s that has superior mechanical and durability performance, this system is fiber-reinforced polymers (FRP). FRP was first used in military and aerospace applications for its novel performance and limited size and weight, then it was utilized in some manufacturing industries such as Automotive, Aviation, and finally the construction industry. The utilization of FRP has increased dramatically in the field of structure strengthening for three main advantages over conventional RC Jacketing technique; minimal time of application, limited space needed, and finally its high durability and long service life which makes a more sustainable system. However, the FRP system still has some limitations in some construction codes, such as its strengthening ratio of total element capacity, and high aspect ratio in column dimensions in some cases. In this study, an attempt is made to develop a model using Anylogic software that simulates the structure repair and strengthening process utilizing both RC Jacketing and FRP systems for three types of structural elements columns, beams, and slabs. This model also can perform cost-time-environmental impact optimization to determine how many structure elements of each type should utilize each of the strengthening approaches, taking into consideration the benefits and limitations of each method. The model was verified by considering a set of data from a project, and the results showed the model's optimization efficiency.

16. Examining the Current Applications and Future Trends in Human–Robot Collaboration in the Construction Industry

    Yewande S. Abraham, Visveish Babu Gajendiran Kamaraj, Abiola A. Akanmu, Chukwuma Nnaji

    Abstract

    In recent years, the construction industry has been undergoing a remarkable transformation with the integration of cutting-edge technologies. One of the important trends in this domain is the emergence of human–robot collaboration (HRC), which has the potential to redefine the traditional standards of construction project execution. Construction automation using emerging technologies such as robots, unmanned aerial systems (UAS), sensors, virtual reality (VR), and augmented reality (AR) has improved the efficiency of construction processes. Strenuous and repetitive construction activities can be carried out by robots thereby improving productivity, enhancing safety and quality, and addressing workforce shortages. Through a literature review, this study discusses the applications, benefits, and challenges of HRC in the construction industry. Key application areas include material handling and transportation, demolition and deconstruction, prefabrication, quality control, site surveying, and site monitoring. The main challenges encountered in HRC include technological barriers, integration with existing workflows, workforce considerations, and cost. Emerging technologies such as advanced robotics, artificial intelligence (AI), wearable devices, and Internet of Things (IoT) integration will reform the construction industry. These technologies enable robots to possess higher levels of autonomy, interact more seamlessly with humans, and collaborate more effectively. As construction processes evolve, understanding the intricacies of HRC applications and their trajectory within the industry becomes critical for stakeholders aiming to enhance productivity, safety, and overall project outcomes.

17. Investigating Spatial Autocorrelation for Petroleum Projects

    Safinaz ElDawody, Yasmeen Essawy, Khaled Nassar

    Abstract

    Geospatial information involves the data that have an implicit or explicit association with its location according to a specific coordination system. This study uses Excel to study the spatial autocorrelation for multiple petroleum projects using specific variables in upstream, midstream, and downstream types of petroleum projects. The aim is to investigate the efficiency of employing this particular technique in the planning and monitoring phases of petroleum projects, while concurrently exploring the involved ways in which the chosen variables influence the ultimate project cost and duration. The reason is that recognizing the presence of spatial autocorrelation can significantly enhance the efficiency of the analysis by helping to identify correlated variables. This allows a more subtle understanding of the relationships between different spatial entities or phenomena. By detecting spatial autocorrelation for petroleum projects, decision-makers can better determine how variables are related across space, leading to more robust and insightful conclusions in their analyses. Global Moran’s I is the mathematical method used to examine the data of different locations and assess similar values in nearby areas across the entire region. Moran’s I is employed to determine whether the spatial pattern for the selected projects shows clustering or dispersion throughout the study area. The findings are shown on a map to demonstrate how the discussed variables relate in space visually. At the same time, Moran’s I plot helps determine if there is any pattern in the dataset, ranging from scattered to clustered values or no spatial correlation at all. Some limitations to this technique are addressed and conclusions and suggestions for future research work are discussed.

18. Enhancing Transparency and Accountability in ESG Risk Management Within Public–Private Partnership: A Blockchain-Based Approach

    Navid Torkanfar, Alfredo Narro, Jason Jarrett

    Abstract

    This study examines the integration of blockchain technology in public–private partnership (PPP) projects, with a focus on enhancing transparency and accountability in environmental, social, and governance (ESG) risk management. PPPs play a crucial role in global infrastructure development, addressing economic and societal needs by leveraging collaborations between public and private sectors. However, managing ESG risks in these projects presents significant challenges, particularly in terms of transparency and accountability due to the complex, multi-stakeholder nature of PPPs and the lack of standardized reporting frameworks. Blockchain technology, characterized by its decentralization, immutability, and transparency, emerges as a transformative solution to these challenges. By providing a secure and transparent platform for real-time data access and ESG performance tracking, blockchain can significantly improve stakeholder engagement, enhance governance practices, and align PPP projects with global sustainability goals. The paper proposes a blockchain-based framework to standardize and manage ESG considerations in PPPs, utilizing a decentralized application (DApp) built on Hyperledger Fabric. This approach aims to revolutionize the management of ESG risks in PPPs, fostering more sustainable and equitable development outcomes. The efficacy of this framework will be evaluated through a conceptual case study, illustrating its potential to streamline ESG risk management and enhance the overall accountability and transparency of PPP projects.