Saudi Arabia’s arid and semi-arid regions suffer from water scarcity because of climatic constraints and rapid growth of domestic and industrial water uses. The growing demand for high-quality water supplies and to reduce the dependency on desalination creates an urgent need to explore groundwater resources as an alternative. The weighted overlay analysis method using the fuzzy-analytical hierarchy process (FAHP) multi-criteria decision making (MCDM) techniques combined with geoinformation technology was used in this study to explore the groundwater potential zones in the Itwad-Khamis watershed of Saudi Arabia. Twelve thematic layers were prepared and processed in a GIS setting to produce the groundwater potential zone map (GPZM). Subsequently, potential groundwater areas were delineated and drawn into five classes: very good potential, good potential, moderate potential, poor potential, and very poor potential. The estimated GWPZ (groundwater potential zones) was validated by analyzing the existing open wells distribution and the yield data of selected wells within the studied watershed. With this quality-based zoning, it was found that 82% of existing wells were located in a very good and good potential area. The statistical analysis showed that 14.6% and 28.8% of the total area were under very good and good, while 27.3% and 20.2% were accounted for the moderate and poor potential zone, respectively. To achieve sustainable groundwater management in the Aseer region, Saudi Arabia, this research provided a primary estimate and significant insights for local water managers and authorities by providing groundwater potential zone map.

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.

The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.

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.

The desire to increase resource management efficacy in the construction sector is expanding because of measures to reduce costs, boost productivity, and minimize environmental impact. The Internet of Things (IoT) has the potential to alter resource management in the construction sector by delivering real-time data and insights that may assist decision-makers in optimizing resource allocation and usage. Incorporating Internet of Things (IoT) technology into the construction sector will be investigated in this study to discover how resource management is affected. The aim of the study is to identify the essential aspects that promote optimal IoT integration and to investigate how IoT may influence resource management. The relations between variables and their fundamental elements are investigated using structural equation modelling (SEM). In the context of building projects, the study analyses how IoT integration influences resource allocation and utilization, real-time monitoring, and proactive maintenance. The building sector in Malaysia provides concepts on IoT in resource management. Based on this research's outcomes, there is a distinct association between the utilization of IoT technology and effective resource management in the construction sector. IoT adoption is affected by a multiplicity of issues, including data analytics, data security and privacy, integration and interoperability, scalability, and flexibility. This study contributes to addressing considerable gaps in the corpus of information on IoT technology integration in the construction sector. It analyses how IoT may effect resource management, emphasizing how IoT technology may enhance the efficacy of human, mechanical, and material resources.

The construction sector is garnering increasing concern as an industry that generates significant pollution while suffering from a lack of adequate environmental performance evaluations. The purpose of this study is to evaluate the environmental performance of construction sector industries using the ISO 14031 standard in three dimensions: planning, using data and information, and reviewing and improving environmental performance evaluations. Using a descriptive and correlational approach, this study described and analyzed the level of implementation using a 7-point Likert scale questionnaire distributed to 1000 organizations in Saudi Arabia. The questionnaires were completed between May and December of 2018 and the results were measured against similar studies. The mean positive percentage responses were 31.36 and expressed the implementation levels of the three dimensions of environmental performance. The corresponding values for the three dimensions’ items were 33.36, 29.83, and 30.95 respectively. The minimum Kendall correlation coefficients for each item in a dimension were 0.79, 0.79, and 0.83, respectively. The maximum correlation values between an item and a dimension to which the item did not belong were 0.25. This study answers a fundamental question regarding the implementation level values of environmental performance in construction organizations based on the ISO 14031 standard model and analyzes the pairwise correlation among the descriptor variables of the model. Further studies are needed to investigate the model structure using the principle component, factor analysis, and cluster analysis.