Traffic management and control applications require comprehensive knowledge of traffic flow data. Typically, such information is gathered using traffic sensors, which have two basic challenges: First, it is impractical or impossible to install sensors on every arc in a network. Second, sensors do not provide direct information on origin-to-destination (O–D) demand flows. Consequently, it is essential to identify the optimal locations for deploying traffic sensors and then enhance the knowledge gained from this link flow sample to forecast the network’s traffic flow. This article presents residual neural networks—a very deep set of neural networks—to the problem for the first time. The suggested architecture reliably predicts the whole network’s O–D flows utilizing link flows, hence inverting the standard traffic assignment problem. It deduces a relevant correlation between traffic flow statistics and network topology from traffic flow characteristics. To train the proposed deep learning architecture, random synthetic flow data was generated from the historical demand data of the network. A large-scale network was used to test and confirm the model’s performance. Then, the Sioux Falls network was used to compare the results with the literature. The robustness of applying the proposed framework to this particular combined traffic flow problem was determined by maintaining superior prediction accuracy over the literature with a moderate number of traffic sensors.

Steel I-beams may be subject to deviation from their normal path towards the lateral direction due to obstacles along their axis line. This deviation in the lateral direction, i.e., the out-of-plane distance, affects the behavior of the steel beams and may reduce their ultimate capacity. To obtain this effect, finite element modeling (FEM) was used to model these beams with and without an out-of-plane distance at the mid-span beam length with several different variables. These variables were the out-of-plane distance, cross-section dimensions, beam length, and steel yield stress. The reliability of using FEM simulation was confirmed by comparing the experimental test results of 25 available steel beams in previous studies. The results indicate the high accuracy of the simulation of this beam in terms of ultimate capacity, structural behavior, and deformation patterns. After verifying the results, 116 broad-flange I-beam (BFIB) steel beams with different out-of-plane distances were modeled. The results showed that using an out-of-plane distance equal to the flange width of the BFIB-300 cross-section caused a 60% decrease in the ultimate capacity. The reduction ratios in the ultimate moment capacity in out-of-plane steel beams were directly proportional to the out-of-plane distance, cross-sectional dimensions, and steel yield stress, while the beam length had no effect. Failure in beams containing an out-of-plane distance occurs as a result of a global buckling in the upper flange, which contains tensile stresses at the outer edge and compressive stresses at the inner edge, with stress concentration occurring at the point of contact of the out-of-plane part with the main beam. The prediction results of the design codes were compared with the results of experimental tests and the FEM analysis of the beams with and without out-of-plane distances. For all the beams with out-of-plane distances, all the design codes were unable to predict this ultimate capacity.

Steel beams with eccentric loads are subjected to combined bending and torsional moments that lead to lateral displacements, unwanted stresses at the top and bottom flanges, and global buckling along their length. To resist these displacements and stresses, horizontal stiffeners were used in the direction of the beam axis at locations of the beam’s web height. To conduct this study, Finite Element Modeling (FEM) was used to simulate these steel beams. The reliability of the FEM results was first verified by comparing them with the results of 25 steel beams that had been experimentally tested in previous studies, and the results showed high accuracy in modeling these steel beams. Secondly, a FEM analysis was performed on 70 steel beams, considering certain variables, namely the locations of the horizontal stiffeners relative to the beam’s web height, the width of the horizontal stiffeners, and the reduction in the spacing between the vertical stiffeners. The results showed that locating the horizontal stiffeners closer to the top or bottom flange enhances the beam’s resistance to eccentric loads. The placement of horizontal stiffeners near the flanges influences the stress distribution at their edges and the overall load capacity, with optimal locations at 10%, 20%, and 90% of the web height. Additionally, combining stiffeners at two web height locations increased capacity synergistically, though less than the sum of their individual effects. Using small-width horizontal stiffeners at low ratios of web height achieved similar efficiency to full-width stiffeners at higher ratios, allowing for material savings. Reducing the distance between vertical stiffeners by half also led to similar improvements to using steel beams with horizontal stiffeners of 20% or 90% of the web height. An interaction diagram was developed to predict the ultimate load capacity of steel beams under combined bending and torsion moments with varying horizontal stiffeners.

This study aims to identify and analyze the key factors influencing the successful integration of Internet of Things (IoT) technology in the construction industry to enhance building efficiency and user experience. The integration of IoT technology in buildings has the potential to significantly improve operational efficiency, sustainability, and user satisfaction. However, achieving effective implementation requires a comprehensive understanding of various influencing factors. To develop a structural model elucidating the components critical for IoT integration in the construction sector. Amixed-methods approach was employed, combining qualitative interviews with 12 IoT experts and quantitative surveys involving 112 participants in a preliminary survey and 184 in a main survey. Data were analyzed using exploratory factor analysis (EFA) and structural equation modeling (SEM). The findings highlight the significant impact of data-driven decision-making (β = 0.163, p < 0.001), energy management (β = 0.242, p < 0.001), integration with interconnected systems (β = 0.104, p < 0.001), personalized user experiences (β = 0.247, p < 0.001), and predictive maintenance (β = 0.241, p < 0.001) on optimizing IoT utilization in buildings. The model demonstrates a good fit with a predictive relevance measure (Q² = 0.197). The study provides valuable insights for industry professionals and scholars, offering a framework to guide future IoT technology applications in construction to achieve enhanced efficiency and user satisfaction.

The increasing focus on sustainability in the construction industry has emphasized the need for innovative materials such as Polyurethane Coatings (PUCs) to enhance the durability and performance of construction projects. Despite their potential benefits, including fire resistance, water resistance, and flexibility, PUCs are primarily utilized in large-scale projects, leaving a significant gap in their adoption in small to medium-sized construction initiatives. This study aims to evaluate the key success factors for the effective implementation of PUCs in construction projects and their impact on performance, sustainability, functionality, and aesthetics. A mixed-methods approach was adopted, beginning with a pilot survey to identify success factors from existing global literature. Exploratory Factor Analysis (EFA) was conducted on the pilot data, resulting in the identification of four critical constructs. A quantitative survey followed, targeting construction project personnel, with data analyzed using Structural Equation Modeling (SEM) to develop a structural model. The results demonstrate a significant relationship between sustainability and the successful implementation of PUCs, supported by robust model reliability and validity. This research addresses the theoretical gap in understanding the broader application of PUCs and provides actionable insights for stakeholders aiming to integrate these coatings into sustainable construction practices. The findings offer a framework for future research and practical recommendations for expanding the adoption of PUCs, particularly in small to medium-sized projects, to drive sustainability and innovation in the construction sector.

The application of blockchain technology is being identified in this construction industry revolution, particularly on high-rise projects. However, there is a significant research gap in understanding its applicability to enhance structural integrity, risk management, and sustainability. This study will try to bridge this gap by researching the impact of blockchain on these critical areas. Stakeholder data were obtained from a questionnaire of standard questions and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results show that blockchain adoption is positively correlated with significant improvement in data security (0.265, p < 0.001), structural integrity (0.358, p < 0.001), sustainability (0.301, p < 0.001), and risk management (0.318, p < 0.001). These findings suggest that the technology can lead to safer, more sustainable, and better-managed high-rise projects. In such implications, a considerable number of studies is put under consideration since they would present a new avenue through which advanced technologies would be brought into construction to achieve higher standards both in safety and environmental responsibility. Therefore, this present study adds to the existing knowledge on blockchain applications within construction, making valuable insights for industrial professionals and policymakers toward improving project outcomes through adopting technological innovation.