The potential of Industry 4.0 digitization practices to improve sustainability and enhance overall project performance has garnered significant attention in the construction industry. Nonetheless, there is a necessity for empirical investigations that delve into the particular factors and constructs that contribute to this achievement. This research aims to address the existing gap in the literature by examining the favorable consequences of Industry 4.0 digitalization techniques in the context of sustainable construction management. The research utilized a mixed-methods methodology, integrating exploratory factor analysis (EFA) and structural equation modeling (SEM), to examine survey data obtained from the construction sector in China. The survey questionnaire comprised constructs that pertained to sustainability, technology, design, functional aspects, resource management, and managerial efficiency. The concept of sustainability has been identified as the most significant factor in shaping sustainable construction practices. The findings presented herein contribute to the theoretical comprehension of the determinants that impact the execution of Industry 4.0 digitalization methodologies within the construction sector. The interrelated constructs that have been identified provide valuable insights for practitioners and policymakers seeking to utilize Industry 4.0 practices to achieve sustainability, enhance technology adoption, optimize design processes, streamline functional aspects, improve resource utilization, and increase managerial efficiency. Implementing these strategies can enhance project success and ensure long-term sustainability in the construction industry. This research adds to the expanding pool of information regarding the factors that lead to success in Industry 4.0 digitization practices within sustainable construction management.

Accurate rainfall forecasting is crucial for various sectors across diverse geographical regions, including Uttarakhand, Uttar Pradesh, Haryana, Punjab, Himachal Pradesh, Madhya Pradesh, Rajasthan, and the Union Territory of Delhi. This study addresses the need for precise rainfall predictions by bridging the gap between localized meteorological data and broader regional influences. It explores how rainfall patterns in neighboring states affect Delhi's precipitation, aiming to improve forecasting accuracy. Historical rainfall data from neighboring states over four decades (1980–2021) were collected and analyzed. The study employs a dual-model approach: a daily model for immediate rainfall triggers and a weekly model for longer-term trends. Several machine learning algorithms, including CatBoost, XGBoost, ElasticNet, Lasso, LGBM, Random Forest, Multilayer Perceptron, Ridge, Stochastic Gradient Descent, and Linear Regression, were used in the modeling process. These models were rigorously assessed based on performance metrics from training, validation, and testing datasets. For daily rainfall forecasting, CatBoost, XGBoost, and Random Forest emerged as top performers, showcasing exceptional accuracy and pattern-capturing capabilities. In weekly rainfall forecasting, XGBoost consistently achieved near-perfect accuracy with an R² value of 0.99, with Random Forest and CatBoost also demonstrating strong performance. The study provides valuable insights into how climate patterns in neighboring states influence Delhi's weather, leading to more reliable and timely rainfall predictions.

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.

Construction is dangerous, making safety management essential. Robotics process automation (RPA) can improve construction project risk management. RPA is hindered by several factors. This study examined the primary technical, economic, legal, privacy, and resource obstacles to RPA adoption for tall building safety management. The pilot survey comprised 161 Malaysian tall building specialists, while the full questionnaire poll included 231 experts. EFA and SEM analyzed the data. Technology, economics, legislation, privacy, and resources prevented RPA from managing tall building safety. Theoretical and empirical breakthroughs in construction safety management and RPA deployment prompted this inquiry. This study illuminates the main obstacles to employing RPA for tall building safety management. The results show where to spend time and money to eliminate the obstacles. The study’s management implications may benefit construction safety managers, project managers, and company owners. The findings may help the building industry plan RPA safety management in tall projects and overcome hurdles. This study contributes to construction safety management and RPA deployment theory by identifying and analyzing the main barriers to using RPA for safety management in high-rise buildings. This research can help solve the problems preventing RPA from being used in construction project safety management.