Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 13

This paper proposes a novel approach for the seismic evaluation of reinforced concrete shear walls in existing buildings. The approach builds on the well-known nonlinear time-history analysis and provides a probabilistic lens to assess the safety of existing cores based on the failure pattern and likelihood of occurrence. The proposed framework was implemented for a real-life application, and reliability indexes were determined for various failure scenarios of a concrete core in an existing building. The results suggest that for some failure scenarios, elements within the shear core that were not initially identified to form a shear failure may be part of the critical failure path of the wall and must be upgraded. Therefore, a comprehensive reliability analysis is important for making informed decisions regarding the repair and maintenance of existing structures subjected to seismic loading. The framework can also be applied to other structural elements to determine the load model for the reliability analysis.

The NGA-West2 ground motion prediction models (GMPMs) are widely used to estimate shallow crustal ground motions at a given site based on several predictor variables such as earthquake magnitude, distance, and site characteristics. These GMPMs also play a key role in the construction of target response spectra for record selection. With the introduction of target response spectra such as the conditional mean spectrum (CMS) and conditional spectrum (CS) which are not only site-specific but are also structure-specific, there is a greater need for structural engineers to use GMPMs than before. However, many of the input parameters of GMPMs require having a strong knowledge of the seismicity of the site and, therefore, are often unknown to engineers. Engineers typically have to make assumptions to determine the unknown predictive variables such as dip angle, hypocentral depth, and rupture width which may affect the accuracy of the seismic performance assessment. In this study, the effects of these assumptions on record selection are investigated in terms of the spectral acceleration amplitudes and overall spectral shape of the CMS/CS target spectra. For this purpose, a parametric study is conducted by constructing a series of CMS/CS for a single-degree-of-freedom system based on the NGA-West2 GMPMs with different estimated source, path, and site parameters. Using these target response spectra, different sets of records are selected and compared. Also, the impact of the estimated predictive variables on the collapse capacity of the system is evaluated. It is found that the overall shape of the CMS/CS and the resulting selected records can be considerably affected by using the estimated input parameters when the exact values are not known. The significance of the effect of input parameters depends on the type of record selection method, the conditioning period, and the criteria of the earthquake causal parameters.

In 2018, the results of a Climate Change Adaptation Standards Inventory Analysis conducted by the Canadian Standards Association (CSA) indicated cavity-wall design as requiring urgent climate change adaptation provisions. Distress in cavity-walls is often attributed to excessive differential movements due to moisture-induced volumetric variations, producing cracks in outer clay brick veneer and compromising durability, which may increase with climate change—test data are, however, still scarce, and only refer to individual blocks/bricks rather than mortared masonry samples. To address these knowledge gaps and devise climate change-adapted design alternatives for cavity-wall structures, a comprehensive research project has recently been launched at McGill University, co-sponsored by Natural Sciences and Engineering Research Council (NSERC), Mitacs, the Canadian Masonry Design Centre (CMDC) and the Canadian Concrete Masonry Producers Association (CCMPA). The latter focuses on the design challenges and structural implications related to the use of wider cavities (to augment thermal insulation) through mechanical and environmental tests, as well as numerical modelling, vital for identifying potential criticalities and opportunities in such new climate-adapted designs. In this paper, we present preliminary results from structural and environmental laboratory characterization studies on modern concrete masonry assemblies, including uniaxial compression tests on masonry units, doublets, triplets and prisms, but also innovative moisture tests specifically conceived to monitor the volumetric variations of both units alone and masonry specimens under controlled temperature and relative humidity, therefore providing a more accurate overview of drying shrinkage. Produced data, combined with those collected in the next research stages through additional tests also on clay brick veneers and building-scale numerical modelling, will enable us to evaluate the combined effects of climate change and multiple design alternatives to meet more stringent sustainability goals, vital for ensuring the durability of the next generation of masonry constructions.

This paper puts forward an image-processing-based method for detecting damaged buildings using post-disaster imagery data. Depending on the level of a disaster, manual inspections could be time-consuming, expensive, and risky. Rapid assessment of damaged buildings is crucial for proper rescue and reconstruction planning in the aftermath of a disaster. As a common image collection tool after disasters, this study focuses on using unmanned aerial vehicles (UAVs), ensuring the capability of the proposed method for automatic assessments. Most of the previous studies on rapid building damage detections required pre-disaster images as inputs, which limits their application to locations with such prior information. However, this study utilized only the post-disaster images to develop a tool capable of detecting damage level of buildings. Insufficient imagery data was the main challenge facing previous studies in this field. Thus, novel features are defined based on edge detection and texture analysis of images to capture damages in the buildings more accurately and decreasing the need for larger training datasets. These features are then employed in a Naïve Bayesian classification method to account for the prevailing uncertainties. The proposed method is verified using real-life images. Such model, in conjunction with an automatic data collection using UAVs, will make it possible to have a rapid assessment and prioritization for rescue and reconstruction planning in the aftermath of a disaster.

Laminated veneer lumber (LVL) is an engineered structural composite lumber that is used in structural framing applications in residential and commercial construction. Because LVL is commonly used as structural members in applications that could be exposed to moisture and freeze–thaw cycles, there is a need to better understand the behaviour of the material under harsh environmental conditions, including studying the influence of freezing–thaw cycling on its bending strength. To address this need, this paper presents experimental results on the structural performance of four large-scale LVL beams tested in dry conditions as well as after being subjected to freeze–thaw cycles over a period of two weeks. The 3.25 m long beams were tested under four-point loading to evaluate their bending strength, stiffness, and failure mode. The beams were also instrumented with distributed fibre optic sensors to evaluate the strain distributions along the length of the beams. Experimental results demonstrate that the freeze–thaw cycles had little-to-no effect on the structural capacity of the LVL beams. Distributed fibre optic sensors were found to be an effective approach for capturing the complete strain distributions along the length of the beams and could be used to identify structural defects in the timber as well as to measure the beam curvature throughout the experiments. Overall, results of this study demonstrate the effectiveness of LVL in situations in which it could be exposed to freeze–thaw cycles.

Timber concrete composite (TCC) floors are increasingly becoming popular in the construction of mass timber buildings due to their enhanced flexural stiffness and improved vibration performance compared to timber floors. Predicting the effective flexural stiffness of TCC floors is vital to calculate the floor deflection or frequency accurately. The most popular method in predicting the effective flexural stiffness of TCC floors is the Gamma method outlined in Eurocode 5–Annex B. Although the Gamma method is generally recommended for predicting the effective flexural stiffness of timber composite floors, its accuracy for TCC floors with various connection systems has not been well established. The current study aims to assess the accuracy of the Gamma method for predicting the effective flexural stiffness of TCC floors by conducting a comprehensive literature review. The measured flexural stiffness values, extracted from the literature, show that the Gamma method can predict the flexural stiffness of TCC floors with an average error of approximately 14% if the shear stiffness of the connection has already been determined experimentally. Since shear tests cannot always be conducted to measure the shear stiffness of a specific connection, engineers resort to analytical equations to estimate it. Some commonly used analytical equations to estimate the shear stiffness of a range of connection systems (e.g., screw, glued, and notched connections) are discussed for this purpose. Experiments under different loading conditions are recommended to determine the accuracy of the Gamma method in predicting the flexural stiffness of TCC floors using the shear stiffness estimated by commonly used analytical equations.

The deterioration of non-structural concrete elements in harsh environments is a major issue for Canada’s cold regions. With conventional steel reinforcements being prone to corrosion and due to climate change-induced projected alteration in temperature, precipitation, and freeze–thaw patterns, the use of more durable reinforcing materials to avoid premature cracking in non-structural concrete components is desirable. In this paper, we investigate through mechanical testing the potential advantages of utilizing either low-ductility fiberglass grids with stiff polymeric coating or high-ductility polymeric geogrid as reinforcing layers to reduce crack opening and increase the flexural performance of plain concrete beams. A total of nine concrete beams with dimensions of 550 × 150 × 150 mm were thus prepared and tested under four-point bending, and their flexural behaviour was monitored in terms of load–deflection relationship, energy absorption capacity, and failure modes. Test results indicate that the use of stiff polymer-coated fiberglass reinforcement can significantly improve the flexural capacity of plain concrete compared to their polymeric counterparts. Similarly, it was observed that fiberglass-reinforcing solutions also provided superior resistance to cracking and post-cracking in comparison with control (plain) beams.

Masonry is a heterogeneous material with complex behavior. Analyzing the behavior and response of this material is complicated because several parameters, such as the strength of individual elements and the bond between blocks and mortar, affect the properties of masonry. Since the strength of mortar plays an important role in the properties of masonry, international standards, such as CSA-A179 and ASTM-C109, provided a compressive test approach to determine the compressive strength of fully cured mortar. On the other hand, there is a critical time within a few hours after the construction of masonry, when mortar is fresh, and its strength is less than the strength of fully cured mortar considerably. Therefore, fresh masonry structures are vulnerable to lateral loads. However, the above-mentioned compressive tests should be conducted at least 7 days after construction. Therefore, there is no validated data regarding the compressive properties of fresh mortar. The aim of this research is to find out the compressive properties of fresh mortar to address this gap. Totally, 140 cubic mortar samples corresponding to different curing time groups, including 3, 6, 18, 24, 72, and 168 h have been tested to assess the effects of curing time on the strength and modulus of elasticity of fresh mortar. The results show that the compressive strength of fresh mortar (younger than 6 h) is less than 2% of the strength of fully cured mortar samples (7 days samples), and curing time plays an important role in the properties of fresh mortar. The mortar strength increases logarithmically, and the strength of 72 h samples is almost 60% of the strength of fully cured mortar samples. The failure mode and behavior of the material before and after 18 h are completely different, and the damage moment of the early-age samples cannot be determined using the stress–strain plots. Therefore, the surfaces of the samples should be monitored to record the crack initiation moment to find out the strength of the fresh mortar.

Stone and brickwork masonry arch bridges constitute historic landmarks and are an integral part of the road and railway transportation systems in Canada. However, the accurate prediction of their structural behavior and load-carrying capacity is a challenging task, given the complexity of the material behavior, geometrical features, and the soil-structure interaction phenomenon taking place between the masonry and soil backfill. Typically, continuum-based approaches (e.g., standard finite element analysis) may fail to simulate the influence of the morphological features of masonry systems and the discontinuous nature of the material. This research aims to provide an in-depth understanding about the computational modeling of masonry arch bridges by adopting more suitable structural analysis approaches, namely, limit state and mixed discrete-continuum analyses. The latter enables a detailed representation of the structural components in a masonry arch bridge, including soil backfill, arch barrel, and spandrel walls, in a 3D setting based on the discrete element method (DEM). In contrast, limit state analysis adopts rigid blocks with active/passive soil pressure applied on the extrados of the arch barrels. Both numerical modeling techniques are adopted to simulate one of Canada’s oldest multi-span stone masonry arch bridges, the Pakenham Bridge (Lanark County, ON, Canada). In the mixed discrete-continuum approach, a progressive procedure is followed where the level of complexity in the computational model is gradually enhanced. First, the arch skeleton is analyzed, and then the effect of soil backfill is captured by adding a continuous medium within the framework of the DEM. Finally, the spandrels walls are included to examine their out-of-plane deformation under the serviceability and ultimate state conditions. The results indicate the differences between 2 and 3D analyses and highlight the influence of soil backfill on the load-carrying capacity of arch bridges. Furthermore, important inferences are made regarding the modeling techniques and the macro behavior of the analyzed multi-span stone masonry arch bridge.

Routine visual structural inspection is a tedious but vital part of structural health monitoring. Many researchers have proposed novel methodologies to automatically classify, detect, and segment structural defects (i.e., crack, spall, etc.) from images. Yet, the scale from single images is ambiguous, which is an important prior for severity classification. While monocular depth estimation is an ill-posed problem, deep learning methods have made great progress field, greatly spurred by a plethora of open RGB-D datasets. However, to the best knowledge of the authors, there is no RGB-D dataset in the civil engineering domain. In this work, the authors seek to develop an efficient method to build an RGB-D dataset for the civil research community. The authors review popular RGB-D data collection paradigms and propose a LiDAR-based data collection method. Finally, the authors use the collected data to create a deep convolutional monocular depth estimation model for defect quantification. The authors hope this work can help other researchers incorporate depth information in their projects and to create a community to share civil RGB-D data to help advance the state-of-the-art in the automated visual structural inspection.

Controlled-rocking piers have gained popularity over the last few decades to achieve enhanced seismic performance for bridge structures. Past research on this system has focused on concrete rocking bridge piers. Recently, rocking bridge piers made from tubular steel sections have been proposed as a possible cost-effective alternative to concrete piers. A steel rocking bridge pier comprises a circular steel tube with welded circular plates at the top and bottom ends, post-tensioned tendons, and energy dissipaters at the rocking interface(s). During a seismic event, lateral displacement of the bridge superstructure is accommodated by means of gap opening and closing at the rocking interfaces located at the top and bottom ends of the columns. Upon column rocking, the gravity load is no longer evenly distributed over the column cross-section, which induces stress concentration and flexural demand on the column that can detrimentally affect its compressive resistance. This article presents a numerical investigation using three-dimensional continuum finite element analysis that was performed to evaluate the effect of column rocking on the compressive resistance of tubular steel bridge piers. The influence of key parameters is examined, including the column diameter-to-thickness ratio, the thickness and overhang dimension of the end plates, the axial load ratio, and the tilt angle of the column. For the range of values considered for those parameters, the study shows that the loss in compressive resistance compared to a vertical column can vary from 13 to 58%, and the most influential parameter is the column diameter-to-thickness ratio.

Glass fiber-reinforced polymer (GFRP) rebars are a more corrosion resistance alternative over steel rebars for concrete structures in coastal areas and cold climate regions such as Canada. However, an existing challenge for the implementation of GFRP rebars is their brittle nature. In particular, for the use of GFRP rebars as the main longitudinal rebars in columns, Canadian Standards Association (CSA) and American Concrete Institute (ACI) ignore or underestimate their contribution. Significant variations in test results and lack of data in this area are the main reasons for making this conservative decision. However, the new trend of CSA is toward considering the participation of GFRP rebars in compression thanks to the experimental efforts that have been taken so far. Therefore, further investigation and test data are required on GFRP rebars and GFRP-reinforced concrete (RC) columns to improve knowledge to completely overcome the lack of confidence in the contribution of GFRP rebars in compression. In this study, previous research efforts on the compressive behavior of GFRP rebars are reviewed. Consequently, fifteen test specimens of GFRP rebars were prepared and tested using the only standard test method that is proposed for rigid plastics by ASTM D695-15. Although test methods have been introduced recently for testing the GFRP rebars in compression, some researchers still prefer using the only available ASTM method. The aim is to obtain a broader understanding of the limitations and the range of outputs compared to the literature. As a result of the tests, a compressive to tensile strength ratio of about 44% on average was obtained. The failure of the rebars mostly started at the ends which are most susceptible to damage. Some specimens prematurely failed due to very local stress concentrations while in the others longitudinal splitting was the observed mode of failure.

Initial costs of materials are conventionally the deciding factor for material selection of structural constructions. However, recent years have seen a tremendous increase in maintenance costs of existing infrastructure that has demanded an alternative approach for material selection in place of the initial cost. The construction industry is now adopting more holistic approaches considering the long-term financial and environmental implications of their projects. Thus, materials such as aluminum with higher initial costs are gaining popularity due to their multitude of positive attributes such as lightweight, durability, low maintenance, excellent recyclability, and corrosion resistance, which can reduce the life-cycle cost of structures over the entire service life. However, the current literature is lacking evidence of a comprehensive life-cycle cost analysis on an existing aluminum structure. Based on this premise, this study performs a life-cycle cost analysis on the first all-aluminum bridge in Arvida, Quebec. The analysis takes into account the construction cost, the available maintenance cost from the past 70+ years as well as the projected costs for future maintenance work, associated traffic disturbance, and the demolition of the bridge at the end of life. An in-depth analysis has shown that the cost for concrete deck rehabilitation, which is more than the initial construction costs, has contributed significantly in the total costs. It is also revealed that in order to obtain conservatives estimation of the total life-cycle cost, LCCA should be performed probabilistically incorporating all major sources of uncertainties.

The use of aluminium alloys for bridge decks has gained significant interest in recent years due to the material’s high strength-to-weight ratio, durability (i.e. high resistance to atmospheric corrosion), and low maintenance requirements compared to the traditional deck material such as reinforced concrete. Moreover, the material’s extrudability property provides designers with the ability to optimize sections by placing the material in areas that maximize structural properties and joinability. For roadway bridge applications, one of the most practical and cost-effective solutions consists of aluminium deck panels, made from welded multi-void extrusions and supported on steel I-girders. In the traditional deck-on-girder bridge design, it is desirable to develop a slip-resistant connection between the bridge deck and the supporting girders to avoid unexpected instability due to fatigue or vibrations. Developing a slip-resistant joint between hollow aluminium deck panels and steel I-girders presents unique challenges, including a lack of access to the interior of the deck panel for the installation of pretensioned bolts and the behaviour of dissimilar metals under mechanical and thermal loads. In the present study, the suitability of a commercial blind bolt type was examined by testing a steel–aluminium plate assembly to evaluate the slip and relaxation characteristics of the joint at ambient temperature. Results of the study indicate that the use of the selected blind bolt is suitable for a slip-resistant aluminium–steel joint. The rapid initial joint relaxation can be reduced significantly by retightening the bolts, 24 h after initial installation.

The use of a large number of reinforcing bars in one layer in reinforced concrete (RC) flexural elements can create difficulties for concrete consolidation on-site. Using larger bar sizes instead of smaller bars to provide the same reinforcement ratio is not always a practical solution because of their supply shortage and their lower perimeter, which increases their development and splice lengths. Placing the same reinforcement in multiple layers will reduce the capacity of the RC section in serviceability and ultimate limit states and can add complexity to the bar placement on-site. In such situations, bundling the rebar can be a practical solution. Glass fiber reinforced polymer (GFRP) rebars are used in applications where corrosion of steel rebars in RC structures is of concern. Most of the research on bundled reinforcing bars was conducted on steel rebars. However, there is a lack of knowledge about the bond behavior and anchorage capacity of bundled GFRP bars. This research aims to investigate the effect of bundling on the pullout capacity, bond–slip behavior, and bond strength of ribbed GFRP rebars in concrete. To achieve the objectives of this research, 12 pullout specimens, including 6 individual and 6 bundled GFRP rebars, were cast and tested. The specimens were cast in dimensions of 200 × 200 × 200 and 300 × 300 × 300 mm. Ribbed GFRP rebar of 16 mm (No. 5) and 25.4 mm (No. 8) diameters and 9.5 mm (No. 3) and 12.7 mm (No. 4) diameters were used for individual bar and three-bar bundle specimens, respectively. During the test, the load and slip of the rebar were measured and recorded. The experimental results were analyzed, compared, and discussed in terms of the pullout strength, bond–slip behavior, and failure mode. Based on the experimental results, all the specimens experienced brittle concrete splitting failure. Similar to bundled steel reinforcing bars, the experimental results showed that the equivalent sectional area method provides close predictions for the pullout capacity of bundled ribbed GFRP bars when compared to corresponding individual bars. The bond strength of the bars and concrete increased noticeably as the dimensions of the specimens increased.

Regular monitoring and maintenance of bridge infrastructure requires sophisticated sensors, expensive equipment, high installation, and labor costs. Specialized equipment required to continuously monitor a large inventory of bridges can result in an alarmingly overspending budget. The concept of indirect bridge health monitoring (iBHM) has been explored by researchers worldwide for efficient monitoring of bridges. iBHM utilizes the traffic passing over the bridge as an actuator and a data-collecting device. The advancement of sensing and communication technologies over the years can be leveraged to enhance the idea of iBHM. The majority of passing vehicles traveling over a bridge is equipped with a smartphone that contains sophisticated sensors such as gyroscope, accelerometer, etc. This study is aimed at using smartphone sensors from passing vehicles to detect the bridge dynamic parameters. A smartphone application is developed in this study capable of gathering and analyzing vibration data through the passing vehicle. The analyzed data is readily available for the user in terms of bridge modal frequencies. The application is capable of simultaneously collecting and visually representing the data throughout the test duration. Furthermore, the smartphone application is linked to a server through the internet for further time–frequency analysis. The performance of this framework was tested using a comparison of direct and indirect data collected using the smartphone application. This study combined smartphone sensing technology with cloud computing to provide a robust monitoring framework for smart infrastructure.

Glass fibre-reinforced polymer (GFRP) bars are used as internal reinforcement in several structural applications, while research is currently developing in this field. Although the use of GFRP rebars in bridge applications is increasing, more experimental data needs to be collected on the behaviour of GFRP bars under fatigue loading. This paper presents and discusses the results of 9 ribbed GFRP bars tested under low-cyclic tension–tension fatigue under different stress levels. A detailed comparison is made between the available experimental fatigue data tested under high frequencies and low-cyclic fatigue tested in this study. The main advantages and disadvantages of this type of test are discussed.

Pipeline cracks are a significant threat to the safety and reliability of energy infrastructure, requiring regular inspection and repair. However, repairing all possible cracks can be costly and time-consuming, especially if some cracks are not critical. This study proposes a method for calibrating crack depth thresholds to determine repair policies. The proposed method uses value of information (VoI) analysis to assess the value of obtaining additional information about the criticality of a crack beyond a certain depth threshold. The optimal crack depth threshold is determined by maximizing the VoI while satisfying safety criteria specified in codes for safety. Unlike previous studies that used VoI analysis for inspection planning, our proposed method optimizes the VoI to determine practical pipeline repair policies, which does not require probabilistic analysis (e.g., reliability assessment) to make a decision for repair. The presented method is applied to a numerical example in this paper, and sensitivity analysis is conducted to show how the optimal threshold varies with repair-to-failure cost ratio and prior information about the crack size. The optimal crack depth threshold for repair aims to balance the trade-off between the costs of repairs and the failure risk of pipe failure caused by a single critical crack. Results confirm that accurate prior information on factors such as crack size and inspection tool performance is essential for determining the optimal threshold. The study’s findings have important implications for the pipeline industry, where optimal repair policies based on VoI can help minimize maintenance costs and failure risks after inspection.

Unreinforced masonry walls are particularly vulnerable to horizontal in-plane loading due to their low tensile strength. The composite nature of masonry leads to significant challenges in predicting in-plane resistance of unreinforced masonry wall. Moreover, the uncertainties in the masonry material may lead to a large scatter. However, quantifying the uncertainty (e.g., estimating the mean value of in-plane capacity) is particularly challenging due to the computational cost. In this study, high-fidelity model and low-fidelity model are integrated into the multi-fidelity framework, leveraging the information from both models. The HF model is developed based on the simplified micromodel approach for masonry structures, whereas the design code-based model is adopted as the LF model. Variance reduction and accuracy improvement are obtained in the multi-fidelity estimation compared to classical Monte Carlo estimation, providing a huge potential for the uncertainty behavior quantification for masonry walls.

This study compares the effectiveness of experimental modal analysis (EMA) and operational modal analysis (OMA) in extracting the modal parameters and damage detection of a concrete beam under different loading levels. The study used a concrete beam of 200 mm in width, 250 mm in depth, and 2200 mm in length. The results showed that the extracted natural frequencies decreased as the damage level increased, and both EMA and OMA were able to capture the modal properties of the structure. The EMA method could not capture many high frequencies in damaged conditions, leading to inaccuracies due to noise in the captured data. On the other hand, OMA tests could extract more modes. However, this method required more sensors, which could affect the cost of the tests. Overall, the OMA tests were more straightforward and practical, resulting in more reliable outcomes. Different damage detection and localization methods were utilized, and their efficiencies were investigated. These methods included modal assurance criterion (MAC), the modified total modal assurance criterion (MTMAC), the coordinate modal assurance criterion (COMAC), the mode shape curvature method (MSCM), and the modal flexibility assurance criterion (MACFA). The study found that relying solely on parameters not considering the frequency change for damage detection may not provide an accurate damage assessment method. Additional techniques such as MTMAC and MACFA can help provide a more comprehensive understanding of the overall stiffness changes in a structure due to damage. It was concluded that in OMA tests, COMAC factors might detect the changes in beam stiffness at degrees of freedom and be able to detect, locate, or quantify the damage. However, the damage localization using the above methods failed in the EMA test due to noise. Overall, this study provides valuable insights into the effectiveness of EMA and OMA in extracting modal properties of concrete beams and the limitations of common damage detection methods.

Vibration-based structural health monitoring (VBSHM) techniques based on tracking operational modal analysis (OMA) results in time are very promising. However, a good understanding of the sources of variability is required to improve the sensitivity of the damage identification algorithms that are based on detecting the smallest variations in modal properties. One important source of variability for bridges situated over ice-covered waters is the resulting ice–pier interactions. This study aims to quantify the effects of ice presence and interactions on the operational modal estimates of bridges. The Confederation Bridge health monitoring program and its associated database serve as the backdrop for this study. There are many instruments (tiltmeters, anemometers, accelerometers, cameras) that have been installed on different parts of the bridge (deck, piers, and box girders) in the past 25 years that have collected important information and have helped to build a significant database which describe the structure’s behavior under actual environmental and operational loadings. This paper presents a novel application of a machine learning technique used to identify ice–pier interactions from the measured pier tilts. To validate this new technique, the ice-monitoring data captured at piers 24 and 32 during the month of February 2017 are taken as a basis for this study. Traditionally, the ice loads have been estimated from the average tilts measured by the tiltmeters installed in the piers using established empirical equations that account for wind effects and pier and foundation stiffness. With the new proposed data-driven method, the ice presence and the interaction effects will be obtained from the residuals of a multiple regression model which accounts for wind speed, wind direction, and temperature. Preliminary results confirm the suitability of the new method. In the second part of the paper, correlations between the estimated ice-induced tilts and the modal frequencies extracted from the analysis of vibration data are determined. The relationship observed in some specific modes will further improve current vibration-based damage detection techniques.

Composite steel beams with precast concrete hollow core (PCHC) slabs are widely used to construct multistory long-span structures. PCHC slabs are commonly manufactured by extrusion or slip-formworks using concrete with very low workability. Initially provided with circular voids, their sections have been improved in the last decades using a kind of ellipsoidal voids and pre-stressed concrete. Compared to solid concrete slabs, PCHC slabs are more light-weighted, cost-effective, easy to install, fire resistant, and have better thermal insulation. Shear connectors ensure the longitudinal shear transfer between the concrete slab and the steel beam in conventional composite beams with solid concrete slabs. In order to quantify the composite behaviour between the steel beam and the concrete slab, it is of great importance to determine the shear stud stiffness and strength values. However, limited studies investigate the mechanical properties of the shear studs in composite beams with PCHC slabs. This paper conducted pushout tests on five full-scale composite beams of 254 mm depth PCHC slabs connected to the steel beam via 19-mm diameter connectors. The shear stud configurations were varied. The effect of cyclic loading on the failure mode was studied. The shear stud strength and load-slip diagrams were investigated by varying the compressive strength of the concrete cover under monotonic and cyclic loading. Also, a finite element (FE) model was developed and calibrated based on the experimental results. The parametric FE analyses were performed to investigate the effect of the shear stud diameter and concrete compressive strength on the shear stud capacity. The experimental and numerical study results showed that the specimens with a three-shear stud layout performed better than those with a two-shear stud layout. Also, the cyclic loading resulted in a 15% lower maximum shear stud capacity than the corresponding monotonic loading.

The structural design procedure of buildings goes through time-consuming iterations, especially at the preliminary design stage. A structural layout is predefined based on the designers’ experience through a trial-and-error approach that might not yield an optimal design. This procedure becomes more complicated for tall buildings where an adequate Main Wind Force Resisting System (MWFRS) should be designed. MWFRS design includes both layout (e.g., cores or peripheral shear walls) and structural element sizes (e.g., reinforcement ratio, shear wall thickness, and shear wall length). This paper presents a multi-objective topology optimization framework for tall buildings subjected to dynamic wind loads to find an optimal shear wall layout based on the required objective functions (e.g., number of shear wall elements). This framework extracts dynamic wind loads time history from a computational fluid dynamic (CFD) model. Then, an automated time history Finite Element Analysis (FEA) is conducted to prepare a database for surrogate model training. An artificial neural network-based surrogate model is built using the prepared database to represent the objective and constraint functions of the optimization problem that can capture the structure response. This model is coupled with a genetic algorithm to identify the optimum layout of shear walls within the predefined architectural and structural constraints. A case study of a 20 stories residential building is presented where constraints of interstorey drift are maintained. The developed framework managed to reduce the weight of the required shear wall elements with an adequate distribution of straining actions and minimum eccentricity.

Many short span bridges can be designed using the Simplified method of analysis for longitudinal load effects prescribed in the CSA S6-19 Canadian Highway Bridge Design Code. The most common structural components used are a concrete slab combined with either prestressed concrete beams or composite steel girders. The most cost-efficient design depends on many parameters that the engineer cannot control. However, the engineer can control some aspect of the design like the material used, the number of girders, the thickness of the concrete slab, etc. These decisions, usually chosen beforehand, have a major effect on the final cost of the structure. With many possible design combinations, it is difficult to guarantee that the final design is the most economical one. Different metaheuristic optimization algorithms have been used to address engineering design problems with different degrees of success. For this study, a discrete variable optimization algorithm is used based on peloton dynamics that occur during bicycle racing, the Peloton Dynamics Optimization (PDO) algorithm. The objective of the design optimization problem is to minimize the mass composite steel girders of a single span bridge configuration subject to constraints imposed by the Canadian Highway Bridge Design Code (CSA S6-19). The optimization procedure used is based on the ultimate and serviceability limit state’s requirements prescribed in the S6 code. The algorithm optimizes the size of the structural plate elements of the steel girders. Results show the PDO algorithm can consistently find the optimum girder built from three steel plates by minimizing the total mass while satisfying all design criteria of CSA S6-19 code.

Embossed steel plates can be used in many composite construction applications. These applications include composite steel girders with pre-cast reinforced concrete slabs, embossed steel-encased concrete composite beams, and concrete-filled checkered steel tubes. Other applications can introduce embossed steel plates as an alternative to using shear studs such as double skin composite slabs and composite tanks. However, the research work conducted on the contact between embossed steel plates and concrete is limited. In this study, the peel behavior of this contact surface is studied through an experimental program. Pull-off tests were conducted on 18 different specimens using various parameters including the embossment pattern, the concrete strength, and the use of epoxy adhesive. These experiments give insight into the effect of different studied parameters, the peel capacity of this contact surface, and the different failure modes of the specimens. It is found that the embossment pattern has a great effect on the behavior of this interface. Moreover, the use of epoxy adhesion surface treatment leads to enhancing the bond strength between some patterns of embossed steel plates and the concrete. Finally, it is concluded that the peel behavior of the embossed steel–concrete interface can be satisfactory for many potential applications.

This paper presents an autoencoder network designed to detect the severity of bridge damage using vibration signals obtained from passing vehicles. While one signal from a single vehicle may only contain limited information about the structures, a large dataset with hundreds or thousands of signals can provide a substantial amount of information. The network is composed of an encoder with a convolutional layer and a long short-term memory (LSTM) layer and a decoder constructed with a fully connected (FC) layer to regenerate the initial input. In our approach, the particular novelty that sets it apart is the fact that we highlight the utilization of solely acceleration signals from intact bridges for training data, as intentionally damaging bridges to obtain signals for damaged conditions is infeasible and unethical in real-world scenarios. To evaluate the results, a metric called the variance-weighted coefficient of determination (R2) is used to measure the goodness of the reconstruction of input signals and determine the damage severity of the bridge. The testing results indicate a strong linear relationship between the damage level and R2. It should be noted that the vibration signals were collected from closed-form equations and numerical models with different combinations of bridge damage levels and vehicle properties. However, with the rapid growth of smartphones and data transmission technologies, the work can be extended to large amounts of real bridge data using such devices.

Building Components and Cladding (C&C) are important to the performance of these structures during extreme windstorms. Post-damage surveys have revealed that low-rise building roofs are particularly vulnerable to windstorms. Therefore, accurate prediction of wind loads on C&C of low-rise building roofs is essential to increase building resilience and mitigate the risks of damage to these structures. The gust effect factor method, developed based on Quasi-steady Theory (QST), has been widely used to determine design wind loads in many codes of practice, primarily for the along-wind overall response of high-rise buildings. The present study examines the applicability of gust effect factor method for predicting C&C wind loads of low-rise building roofs. The flow fields over roofs are relatively complex, with many types of flow patterns and vortical structures that might alter the Gaussian statistics. One building configuration from the NIST database obtained from Boundary-Layer Wind Tunnel II at the University of Western Ontario was used to conduct the analysis. The skewness and kurtosis of C&C wind loads were examined first to demonstrate the statistical distributions of C&C wind load on low-rise building roofs. In addition, the gust effect factors, which relate the wind load fluctuations to upstream wind turbulence, were presented. The results show that the gust effect factors of wind loads on roof corner zones extend the recommended value of 0.85 in ASCE 7-22 due to the complicated conical vortices. Furthermore, it is observed that the gust effect factors vary with effective wind area in the same way as peak wind pressures suggested in ASCE 7-22 and NBCC 2020. This study highlights that the gust effect factor method can be reasonable for predicting C&C wind loads for large effective wind area. However, it may underestimate peak wind loads due to the neglect of body-generated turbulence.

The benefits of repurposing existing constructions are recognized by provincial legislation for their potential in achieving sustainable development goals. In recent years, adaptive reuse projects in Eastern Canada targeting seismically vulnerable old buildings, especially those once used by local industries and featuring unreinforced masonry members, have significantly increased. When major renovations are planned, however, the objectives set by the National Building Code of Canada require old buildings to achieve the same seismic performance of modern ones. As a result, and because of the lack of ad-hoc guidelines and the limited knowledge of traditional construction methods, local engineers are prompted to design overly invasive retrofits or demolish/reconstruct entire sub-structures. To avoid such unsustainable practices, preserve the integrity of Eastern Canada’s built environment and enable its responsible reuse, this research aims to increase knowledge about the response of old unreinforced brick masonry industrial buildings. These results can aid in the classification of typical old industrial masonry buildings and uncover key structural characteristics and quantify their seismic performance, providing essential yet presently missing data to engineering professionals and researchers involved in seismic upgrading projects. First, archival resources are used to track the evolution of structural systems, architectural features and employed materials at the regional scale, enabling us to identify representative building assets at the municipal level. Targeted onsite surveys are thus conducted to evaluate structural conditions and material properties, as well as to create high-fidelity digital geometries, through visual assessment, non-destructive testing and 3D laser scanning. Informed seismic analyses can then performed via numerical modelling, providing insights on recurrent failure mechanisms, displacement and base shear capacities. In this paper, the proposed holistic methodology is applied to two case study buildings located in Montréal and preliminary results are presented. The results presented herein will allow for identification of more sustainable, less invasive but equally performing seismic retrofit measures, tailored to the unique characteristics of the local old industrial masonry structures.

The ability to mitigate residual displacements and permanent damage, indicators of infrastructure seismic resiliency and serviceability, should be considered in assessing seismic performance. Research has intensified on the implementation of materials such as Shape Memory Alloys (SMAs) and Engineered Cementitious Composites (ECCs) due to their preferred mechanical properties to improve seismic resilience. The superelastic properties demonstrated by SMAs and the enhanced tensile strength and ductility of ECCs compared to normal strength concrete make these materials attractive alternatives for components in the seismic force resisting systems in reinforced concrete structures. Their unique properties, relative novelty, and lack of design guidelines result in uncertainty when designing structural elements that incorporate ECCs or SMAs. This paper investigates influential behavioural mechanisms in the analysis of slender shear walls that incorporate hybrid steel-SMA reinforcing bars and ECC subjected to reverse cyclic loading. The numerical studies are performed using the two-dimensional nonlinear finite element program VecTor2. Amongst the behavioural models found to be influential in the numerical response of shear walls containing SMA bars and ECC are the tension and compression response of the ECC material. The available built-in models for FRC tension were investigated to determine their applicability and accuracy in modelling the response of ECC. Additionally, the influence of local fracture of the reinforcement was investigated. The results indicate that the mechanical properties of ECC may not be accurately captured by current FRC models calibrated using steel fibre specimens and may result in overestimation of stiffness prior to yielding of the structural element.

To illustrate the mode interaction in stability analysis of structures, trusses and built-up columns offer a simple context where global flexural modes of the structure can interact with local flexural buckling modes of the members. Truss and built-up column structures are often modelled under the assumption of zero-moment at connections. However, to capture the effect of local buckling in a 2D truss or built-up column member, FE models need to employ beam-type elements with 3Degrees-Of-Freedom-Per-Node to consider nodal rotations and capture bending behaviour of elements in local buckling modes. In this paper, we model trusses and built-up columns of zero-moment at connections using beam-type elements, by changing the standard FE assemblage procedure. Effect of local imperfections on the global buckling behaviour is examined in case studies, which show that the assumptions on connection type might influence the shape and critical loads for local buckling modes as well as the imperfection sensitivity of the structure. The case studies include snap-through as well as bifurcation-type instabilities. The effect of susceptibility to localized buckling in predicting the buckling and post-buckling behaviour is also examined.

The study of historical constructions faces numerous challenges, namely those related to the variety and heterogeneity of the material, typological, and constructive properties of the assets. Given that every case study must be respected on its singularity, a series of restrictions are usually found when carrying out regional-scale surveys. Due to time and processing constraints, the available resources for documenting wide samples of historic buildings are often exceeded. An example of critical work when working with historical constructions is the characterization of masonry, which frequently constitutes the structural base of these assets. The wide variety of parameters (such as the units, mortars, bonding, and connections) lead to many divergent possibilities, admitting relatively few generalizations and demanding a very straightforward analysis. Nevertheless, strategies exist for systematically characterizing masonries, such as the Masonry Quality Index (MQI), which propose a standardized survey model for on-site data acquisition, facilitating the methodologic description of masonry works. Then, it becomes relevant to use tools for enhancing the campaign survey works, namely through the optimization of time and the reduction of intermediate stages between the on-site observations and their compilation on a database. This paper presents and discusses the design and implementation of an integrated data acquisition and management system based on three components: a Geographical Information System (GIS) database, a cloud storage client, and a mobile app. The workflow demonstrates the pertinency, robustness, and advantages of on-site MQI-based characterizations.