Concrete-filled composite plate walls (CF-CPW), also known as SpeedCore systems, have recently emerged as new seismic force-resisting systems for mid- and high-rise buildings. These wall systems accelerate construction schedules, enhancing the economic viability of projects. To facilitate the adoption of CF-CPW systems in Canada, this paper presents the design and analysis methodology for their implementation. A design example is provided to illustrate the details. The methodology is also applied to forty coupled CF-CPW archetypes with varying configurations and details to assess their lateral response. Modal, pushover, and nonlinear response-history analyses are performed. The assessment results demonstrate that coupled CF-CPW systems exhibit excellent seismic performance and are suitable for use as shear walls in mid- and high-rise buildings. Subduction interface earthquakes, however, are more critical, and in some cases, the performance objectives may not be met.
This study proposes a comprehensive and empirically validated safety performance framework for the construction industry. The framework systematically analyzes the complex interactions between company policy, project conditions, and industrial readiness, and their effects on safety performance and overall construction effectiveness. Data was collected via a structured questionnaire administered to 202 professionals from diverse construction projects, followed by rigorous structural equation modeling analysis using IBM SPSS and AMOS software. Structural equation modelling results supported the final validated framework after excluding a non-significant direct path. Results indicate that improvements in safety performance significantly enhance construction effectiveness, streamlining workflows and elevating project quality. Findings show that firm-level safety culture and industry maturity are prerequisites for sustainable safety implementation. While safety measures primarily yield short-term project benefits, their cumulative impact shapes long-term organizational performance and competitive positioning. This study highlights the importance of integrating proactive safety strategies beyond compliance-based approaches. It offers a strategic roadmap for fostering a holistic, proactive, and sustainable safety culture in the construction industry.
The current construction industry needs effective risk management because it establishes the foundation for delivering projects on schedule and within budget and with results that maintain long-term value. The researchers present a hybrid BIM-based fuzzy logic system which utilizes machine learning to detect critical project risks while providing effective risk mitigation methods. The research study aims to create a data-driven assessment system that enables organizations to evaluate complex risk scenarios and make better decisions about stakeholder relations. The system needs expert judgments to handle uncertainty while it uses Fuzzy Analytical Hierarchy Process to determine which risk factors between cost time quality safety and sustainability should receive priority. The research findings indicate that schedule and cost risks represent the main factors that impact performance while quality and safety and sustainability require different strategies for mitigation. Integrating fuzzy logic, machine learning, and BIM enhances predictive capability and supports more effective risk mitigation.
Extreme wind conditions can influence crash injury severity, yet existing studies often analyze crash outcomes and meteorological exposure separately, limiting their ability to capture weather-sensitive crash contexts. This study proposes a cross-stitch multilayer perceptron model for injury-severity-centred auxiliary learning. Crash injury severity is treated as the primary classification task, while wind speed regression is used as an auxiliary supervisory signal to help the model learn wind-related contextual representations. The dataset integrates crash, meteorological, roadway, driver, vehicle, environmental, and temporal information for 12 115 single-vehicle crashes in Chicago from 2021 to 2023. The total training objective is specified as a primary severity loss plus a weighted auxiliary wind-speed loss, and sensitivity analysis is used to select the auxiliary-loss weight. The best overall performance is achieved at λ = 0.8, outperforming both single-task baselines and the hard-sharing baseline. The final model achieves accuracy = 0.873, sensitivity = 0.858, F1 score = 0.870, and area under receiver operating characteristic (ROC) curve = 0.813 for injury-severity prediction, while the auxiliary wind-speed branch achieves mean squared error = 0.073 and mean absolute error = 0.222. SHapley Additive exPlanations analysis indicates that lighting condition, speed-limit context, seasonal exposure, roadway geometry, and driver/time-of-day characteristics contribute to weather-aware injury-severity prediction. These findings suggest that auxiliary wind-speed supervision can improve injury-severity prediction by helping the model encode wind-related crash context, rather than treating wind speed as an equal co-primary outcome.
Road surface cracks are key indicators of pavement deterioration, requiring accurate detection for timely maintenance. This study introduces a deep learning-based crack detection model using a hybrid U-Net architecture enhanced with a pre-trained ResNet50 encoder, Atrous Spatial Pyramid Pooling (ASPP), and attention gates. ResNet50 captures multi-level features, while ASPP extracts multi-scale contextual information, improving detection of cracks with diverse shapes and orientations. Attention mechanisms refine spatial features and suppress background noise, enhancing subtle crack identification. Trained on the Crack500 dataset (471 images with binary masks), the model incorporates preprocessing techniques like resizing, contrast normalization, and data augmentation to address class imbalance. Quantitative results show superior performance in accuracy, Dice coefficient, IoU, precision, and recall compared to traditional CNNs. Visual analysis confirms robustness under varied lighting and surface conditions. Future work may address challenges like noise, low contrast, and occlusion through advanced augmentation, domain adaptation, and real-time deployment via UAVs or robotic platforms.
This study investigated the change in fracture toughness of two high-performance steels, Q420qDNH weathering steel and Q690 high-strength steel, after pre-corrosion treatment in chloride salt environments. The impact toughness of both steels after exposure to corrosion for varying durations was measured using the Charpy pendulum impact test, and the fracture toughness was calculated based on established empirical formulas. The experimental results indicated that steel corrosion initially progressed rapidly, followed by a gradual decrease in the corrosion rate. The impact energy of the specimens decreased with prolonged corrosion time, and when the corrosion reached a certain threshold, the impact energy fell below the specified requirements, suggesting material embrittlement. Both steels exhibited similar trends in impact toughness degradation over time, with the Q690 high-strength steel demonstrating superior fracture toughness after corrosion. In conclusion, long-term exposure to chloride salt environments significantly affected the impact toughness of both Q420qDNH weathering steel and Q690 high-strength steel.
The cement industry is a major contributor to global CO 2 emissions, primarily due to energy-intensive clinker production. Limestone calcined clay cement (LC3) has emerged as a promising low-carbon alternative to traditional Portland cement. It reduces the clinker factor and associated emissions, providing substantial environmental and economic benefits. This review explores the production process of LC3, emphasizing thermal and mechanical aspects of calcination, and grinding and blending stages critical to performance. The hydration mechanism is analyzed by detailing key chemical reactions, hydration products, and microstructural characteristics. Furthermore, the review discusses the influence of various clay types on reactivity and final properties. Key properties of LC3, such as durability, mechanical performance, and fresh-state behavior, are evaluated. Technical challenges and opportunities for large-scale implementation are examined alongside environmental and economic implications. Finally, future research directions are proposed to address limitations and enhance the deployment of LC3 technologies in sustainable construction.
The rising generation of construction and demolition waste, along with the depletion of natural aggregates, demand sustainable alternatives for pavement construction. The aim of this study is to investigate the performance of cement-treated base and subbase mixtures containing recycled concrete aggregate (RCA), fly ash, and hydrated lime as partial substitutes for cement. RCA substitution levels were 0%–100%, and cement contents (4%–6%) mixed with fly ash + lime (10% + 20% and 20% + 10%). The results include mechanical properties such as compaction characteristics, unconfined compressive strength (UCS), durability index, elastic modulus, modulus of rupture and resilient modulus, and microstructural studies. The results indicate that combining RCA and lime increases the optimum moisture content while causing a marginal decrease in maximum dry density. The 28-day UCS for mixes ranged from 4.86 to 8.95 MPa, meeting IRC:37-2018 parameter requirements; all durability index values were greater than 0.80. Resilient modulus values ranged from 335 to 491 MPa, with stress-dependent behaviour and high R2 = 0.9474. Mixes with 40%–60% RCA, 5%–6% cement, and 10% fly ash + 20% lime showed the best performance. The findings confirm that partial replacement of cement with fly ash and lime enables structurally reliable, environmentally sustainable stabilized pavement layers.
In prefabricated concrete structures, nuts are a typical tool used to anchor the steel bars of pre-fabricated components. How to choose the appropriate nut is the key to achieving reliable anchoring of steel bars in prefabricated components. This study investigates the effects of different nut types (stainless steel flange nut, carbon steel flange nut, combination nut, and anchor plate nut) on the anchoring performance of steel bars with diameters from 8 to 28 mm. A comprehensive program of pull-out tests was carried out to evaluate the failure-to-yield ratio and characterize the failure modes. Subsequently, simplified mechanical model analysis was developed and analyzed for three different failure modes. The test results demonstrated that for specimens with bar diameters ranging from 8 to 20 mm, the stainless steel flange nut exhibited the best anchoring performance, whereas the anchor plate nut performed the poorest. For bar diameters between 22 and 28 mm, the carbon steel flange nut showed the optimal performance, and with the anchor plate nut again being the least effective. Flange nut generally outperformed combination nut, which is attributed to their superior structural integrity. Considering both anchoring performance and material cost, carbon steel flange nuts present the best choice. Therefore, it is recommended to use flange nuts in practical engineering (stainless steel for bars with D ≤ 20 mm, and carbon steel for bars with D ≥ 22 mm) to ensure the safety and durability of anchoring of steel bars.
Portland cement is the most widely used construction material, but there are some limitations to using it to produce concrete, i.e., its durability issues and emission of a high amount of CO 2 in its production. Therefore, in this study, the ground-glass pozzolan (GGP) is used to partially replace the cement for the in situ application. This field project used concrete with a 10% cement replacement with GGP to pour the Darwin bridges elements. These bridges won the 2021 Award of Excellence in the infrastructure category from the American Concrete Institute (ACI). The concrete is examined for the long term with respect to its performance by determining its fresh, hardened, visual, and durability properties. It was found that concrete incorporating GGP demonstrated significant performance, with key properties such as workability, compressive strength, visual, and durability indices exceeding the desirable limits specified in relevant standards.
Booms are flexible retention structures used in water bodies for various purposes, such as stopping ice pieces, accumulating floating debris, and serving as highly visible floating safety barriers. High flow velocities can cause increased dynamic loading on booms, potentially increasing the risk of failure. Therefore, studying boom behavior, especially in highly energetic flows, will help engineers identify potential failure modes and improve boom design. Laboratory-scale experiments were conducted to evaluate the behavior of booms over a range of low flow velocities, extending up to and beyond the onset of complete submergence. Boom sections (or pontoons) were fabricated using polyvinyl chloride pipes, each designed as a partially submerged cylinder with different submerged depths. The pontoons were tested individually by floating them on the water surface of a 13 m long flume. They were secured using cables attached to the ends of the pontoon, extending down through the water at an angle, through pulleys attached to the flume floor, and then attached to a load cell. The load cell was used to measure the time series of hydrodynamic forces acting on the boom at a frequency of 50 Hz. Additionally, an accelerometer was mounted to the pontoon to record its acceleration in three directions, particularly during high-velocity conditions when the pontoon experienced extreme fluctuations. Concurrently, a video camera recorded the pontoon's position, and the resulting time series of displacement was obtained using image processing tools. The time-series data of displacement, acceleration, and load collectively offer a comprehensive overview of the pontoon's behavior. The high magnitude and extreme load fluctuations observed at elevated flow velocities suggest that simplified drag force equations, as recommended in design guidelines, may be insufficient for accurate load estimation. Furthermore, the high frequency of load variation—exceeding 1 Hz in some cases—highlights the importance of considering fatigue failure in boom components due to repeated cyclic loading.
India, with the second-largest road network globally, is undergoing rapid construction, offering benefits but also posing environmental challenges. To address these issues, researchers are exploring industrial by-products to develop sustainable pavements. This study investigates the use of imperial smelting furnace (ISF) slag, a zinc industry by-product, as a sustainable replacement for natural fine aggregate (FA) in concrete pavements. Laboratory specimens were prepared by substituting natural FA with ISF slag from 0% to 100%. Laboratory specimens were prepared by substituting natural FA with ISF slag from 0% to 100% on volume basis. Results showed that incorporating ISF slag enhances compressive and flexural strength up to 60% replacement at 7 and 28 days, after which strength declines. Additionally, the compressive strength of dry lean concrete mixes prepared with an aggregate-to-cement ratio of 10:1 and results indicated that the compressive strength increased with an increase in ISF slag content. Split tensile strength increased from 3 MPa at 0% to a peak of 4.54 MPa at 80%, before decreasing to 4.16 MPa at 100% at 28 days. Life cycle cost and environmental assessments revealed that 60% ISF slag use reduces cost and CO2 emissions. Thus, ISF slag offers economic and environmental benefits for sustainable pavement construction.
This study aims to analyze the pore characteristics of recycled aggregate permeable concrete (RAPC) using percolation theory, providing insights into its permeability and structural properties. RAPC, with its complex interconnected pore system, is an ideal material for exploring percolation behavior in porous media. An orthogonal experiment was conducted to determine the optimal mix ratio of RAPC, examining the effects of aggregate–binder ratio, aggregate grading, and fly ash content on its compressive strength and permeability. Results showed that these factors impact compressive strength and permeability differently, with aggregate grading having the greatest effect on permeability. Additionally, a pore network model of RAPC was constructed to quantify pore size distribution and connectivity. Findings indicate that pore radius and pore throat radius have left-skewed unimodal distributions, while throat length and coordination number follow normal distributions. The results provided a new perspective for understanding the pore structure of recycled concrete.
This paper is part of a special issue related to the seismic performance of buildings, bridges, foundation, and other lifeline infrastructure observed during the 2023 Turkish earthquake. The seismic performance of these infrastructure is highly dependent on the building code design requirements. This paper focuses on the comparative analysis of the evolution of seismic design building codes in Türkiye and its comparison with the Canadian building code. Both nations have transitioned from simplified seismic hazard maps and prescriptive design approaches to probabilistic seismic hazard assessments and performance-based design. Key areas of comparison include the development of design objectives, the refinement of seismic hazard maps, advancements in demand calculation methods, irregularity provisions, and the adoption of dynamic and nonlinear analysis techniques. By examining these codes’ historical development, this study sheds light on major differences and similarities of seismic design codes between these two countries over the years.
Designing structures with ultra-high performance fibre-reinforced concretes (UHPC) requires a thorough understanding of its tensile behaviour. Direct tensile tests provide valuable insights but require specialized equipment available in limited number of research laboratories. Bending tests are more accessible and several inverse analysis methods were developed to estimate UHPC tensile properties. However, each method was developed and validated for specific testing configurations. The inclusion of UHPC in CSA A23.1 and S6 (2019) prompted the adoption of European inverse analysis methods for North American bending tests, with limited success without adequate calibration. This provision was removed in 2025 leaving CSA S6 (2025) without a model for determining UHPC tensile behaviour from bending tests. This paper addresses this gap by proposing a simplified methodology that adapts an existing inverse analysis to alternative testing configurations, enables direct calculation of strain-hardening UHPC pre-peak tensile properties, and improves the reproduction of experimental tensile behaviour observed in 5 UHPC mixtures.
This study explores recycled asphalt mixture (RAM) construction technology using fuzzy decision theory to meet the growing demand for sustainable transportation infrastructure. AC-13 asphalt mixtures were designed, and aged mixtures underwent recycling and mechanical property testing. A research framework integrating fuzzy evaluation of performance indicators and construction parameters was developed. Guided by fuzzy decision theory, macroscopic mechanical tests were performed to assess improvements and predict optimal construction processes. Results show that RAM with fuzzy decision support meets road performance requirements and enhances sustainability. The optimal process involves mixing at 175 degrees C: blend RAP and recycling agent for 15 s, add new aggregate for 15 s, then new asphalt for 20 s, and mineral powder for 25 s. The mixture should be transported within 0.5 h for best performance. This research highlights the potential of fuzzy decision theory to optimize material-technology interactions and provides valuable guidance for RAM construction.
Traffic volume is essential for transportation planning, safety analysis, and infrastructure investment. Achieving comprehensive spatial coverage across road networks faces budgetary and logistical constraints, necessitating reliable estimation for unmonitored segments. This study addresses this gap by developing a multimodal deep learning framework that fuses aerial imagery with parametric data to estimate annual average weekday traffic. Using 749 ground-truth locations spanning over 5000 km of roadway, a citywide case-study is conducted in Edmonton, Alberta, representing one of the most spatially extensive and heterogeneous applications in literature to date. The proposed model is benchmarked against several established methodologies including multivariate linear regression, support vector regression, random forest, XGBoost, and neural networks. Results demonstrate that the framework reduces mean absolute percentage error by 7%-85% relative to these models. Thus, the research yields a robust methodology that integrates multiple data sources to assist transportation agency decisionmaking by providing city-wide traffic volume estimation.
Thermally induced slab curvature, or curling, can cause 24 h variability in the ride quality of jointed concrete pavements (JCP). Locked-in warp curvature may be larger than typical curling curvature and can add to roughness. These curvature effects are often inseparable from persistent structural roughness, including faulting and cracking, in roughness calculations. This study presents a curvature-based framework to back-calculate the effective total slab temperature gradient from longitudinal wheel-path profiles and quantify the portion of the International Roughness Index (IRI) attributable to curl-and-warp slab deformation. Average slab curvature is extracted from field-measured wheel-path profiles after excluding joint-adjacent regions. The measured curvature is matched to predictions from finite-element method simulations using an artificial neural network (ANN). A brute-force ANN search identifies the effective total temperature gradient matching the measured curvature. The back-calculated curvature state generates synthetic profiles, from which a slab-curvature-only IRI component is computed for a 9000 ft JCP test site.
Steel casting technology has enabled the design and manufacture of seismic fuses with enhanced ductility and energy dissipation capacity. Recently, cast steel replaceable modular yielding links, also referred to as cast modular links (CML), were designed and experimentally validated for use in steel eccentrically braced frames (EBFs). To facilitate their application in practice, new provisions for design of EBFs with CMLs were proposed and adopted by CSA-S16:24. This paper presents these newly developed provisions, which are adopted in CSA-S16:24 along with the supporting technical background. Design guidelines and key response parameters of CMLs are discussed in detail. Guidelines for manufacturing and quality control of steel castings used as seismic fuses are provided. The new provisions require qualification testing of CMLs, whose guidelines are outlined. As an example, design of a four-storey EBF with CMLs is presented along with example qualification tests.
The aim of this study is to apply time estimation approaches developed based on metrics such as earned schedule (ES), earned duration (ED), and effective earned schedule (ES(e)) within the framework of earned value management (EVM) project control methodology and to model the data obtained from these approaches using machine learning (ML) methods. Accordingly, a comparative analysis of different ML methods in terms of time estimation accuracy has been performed. A dataset is created using 18 different estimated time at completion (EAC(t)) time estimation methods based on data obtained from a residential construction project in Türkiye. Using this dataset, estimation models were developed through ML techniques. The performance of six ML models employing 18-time estimation approaches was evaluated using six performance criteria. The findings indicate that the GPR model achieved superior accuracy compared to the other methods, demonstrating the strong potential of ML-based approaches for time estimation in construction projects.
