Land use types and anthropogenic activities represent considerable threats to groundwater pollution. To effectively monitor the groundwater quality, it is vital to measure pollution levels before they become severe. In our research area, located in Gilgit Baltistan in northern Pakistan, groundwater supplies are diminishing due to urban sprawl. In this study, we used a GIS-based DRASTIC model (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Hydraulic conductivity) to analyze the area’s hydrological attributes to assess the groundwater susceptibility to pollution. Considering the importance of anthropogenic activities, this research primarily utilizes an adjusted DRASTIC model called DRASTICA, which incorporates anthropogenic impact as a parameter in the model. The resulting map, which depicts vulnerability to groundwater contamination, reveals that 19% of the study area is classed as having high vulnerability, 42% has moderate vulnerability, 37% has low vulnerability, and 2% has very low vulnerability to groundwater contamination. The adopted validation process (nitrate parameter of water quality) revealed that the suggested DRASTICA model achieved better results than the established DRASTIC model in a built-up environment. We used the nitrate concentration in groundwater to verify the formulated results, and the single parameter sensitivity analysis and map removal sensitivity analysis to analyze the model sensitivity. The sensitivity analysis indicated that the groundwater vulnerability to pollution is largely influenced by anthropogenic impact and depth to the water table, thereby suggesting that anthropogenic impact must be explicitly tackled in such studies. The groundwater zones exposed to anthropogenic pollution can be better classified with the help of the proposed DRASTICA model, particularly in and around built-up environments. The responsible authorities can use this groundwater contamination data as an early warning sign, so they can take practical actions to avoid extra pressure on this vital resource.

Non-renewable resources have been becoming scarcer on a global scale by the day, while energy demand has been rising exponentially. To tackle this problem, organizations worldwide have been striving to learn and adopt green practices to sustain themselves and benefit society. In this context, the current study aims to identify and understand the critical factors that encourage individuals working in construction organizations to adopt green behavior. The current study surveyed 121 top managers working in 150 construction firms deployed across Pakistan. It was found that knowledge and awareness significantly contributed to green behavioral adoption. Additionally, behavioral intention, motivation, and environmental consciousness have been found to positively mediate the impact of knowledge and awareness on green behavior adoption. The findings of this study highlight the important factors to consider when developing future policies. Moreover, the research provides a stepping stone for future researchers to evaluate other sectors and regions in similar contexts to draw comparisons and identify areas for improvement.

Effective leadership and creative performance are the predominant factors for the success of modern projects in the global construction industry. However, rigorous research has not explored the nexus between such factors and the leader–member exchange (LMX). To address this gap, this study explores the relationship between dimensions of paternalistic leadership and employee creativity achieved through LMX in the context of the construction industry. Based on social exchange theory (SET), six relevant hypotheses were proposed in this study. The data were collected through a structured questionnaire. An online survey form was used for data collection, through which 288 responses were collected from the construction industry employees working in Pakistan. The collected data were analyzed using Smart PLS in two stages, i.e., measurement model evaluation (reliability analysis, convergent and discriminant validity) and structural model evaluation (R², F², and path coefficient). The findings of the current study reveal a positive association of authoritarian, benevolent, and moral leadership with employee creativity. In addition, LMX significantly mediates the relationship between the two dimensions of paternalistic leadership (benevolent and moral leadership) and creativity, except for authoritarian leadership. Based on the results, this study contributes to the body of knowledge related to the appropriate leadership style in the local construction industry that can be extended to other developing countries with similar dynamics. It also helps the managers target and develops relevant skills to acquire positive outcomes from their team members.

Job stress (JS) is a significant issue in the construction industry of developing countries. This study aims to examine the impact of error-management climate (EMC), safety climate (SC), and psychological capital (PC) (as a mediator) on employee JS in the construction industry, and establish relationships between these constructs. A questionnaire survey was conducted to gather data from 144 respondents. The study’s hypothesized relationships were tested using partial-least-squares structural-equation modeling (PLS-SEM). The analysis indicated a positive association between EMC and PC. Conversely, EMC did not have a negative impact on JS. The study also established a constructive relationship between SC and PC, and a significant negative association between SC and JS. Regarding mediation, PC was found to partially mediate the effect of EMC on JS, accounting for 55% of the variance accounted for (VAF). The study’s innovative contribution lies in exploring the limited research on PC within the construction industry, and investigating the interactions among SC, EMC, PC, and JS.

Organizational climate is the ascribed psychological meaning and significance associated with the procedures, policies, and practices that are recognized and rewarded in the workplace and, hence, mediate the effects of environmental stimuli on individuals’ responses. Safety climate is a specific organizational climate, i.e., organizational climate for safety. Previous research claimed that organizational climate provides a foundation for safety climate, but without elaboration on the foundational mechanisms. This paper attempts to fill this knowledge gap. As organizational climate is a multi-dimensional phenomenon, this paper chooses two dimensions: perceived organizational support (POS) and participative decision making (PaDM). Drawing on an interactive approach to forming climate perceptions, this paper introduces two interactive constructs—leader–member exchange (LMX) and team-member exchange (TMX)—and establishes a multiple mediation model depicting the foundational effect of organizational climate on safety climate. A random sample of 292 Hong Kong-based construction personnel is used to validate the model. The results show that both POS and PaDM are positively associated with perceived safety climate (β₁ = 0.175, p < 0.01; β₂ = 0.502, p < 0.005), both LMX and TMX fully mediate the effect of PaDM on safety climate (effect sizes are 0.146 and 0.076, respectively), and only LMX partially mediates the effect of POS on safety climate (effect size is 0.131). This study sheds light on the foundational effects of organizational climate on safety climate. POS can improve the quality of reciprocal exchange about safety matters between construction personnel and their supervisors and hence raise construction personnel’s awareness of the priority of safety. PaDM can improve the quality of reciprocal exchange about safety matters vertically and horizontally and hence have construction personnel aware of the importance of safety. In practice, this paper suggests that project managers recognize and reward construction personnel’s contributions in a timely manner, genuinely care about their well-being, and take their suggestions seriously in making decisions. In this way, the quality of both vertical and horizontal exchange about safety matters improves, and a sound and positive safety climate ensues.

The use of rubber in concrete to partially substitute mineral aggregates is an effort to decrease the global amount of scrap tires. This study investigates the behavior of rubberized concrete (RC) with various replacement ratios (0–50%) by volume and replacement type (fine, coarse, and fine-coarse) using soft computing techniques. The uniaxial compressive strength (CS), elastic modulus (EM), and ductility (D) are measured, and the effect of rubber content and the rubber aggregate type on the properties of RC is investigated. Scanning electron microscopy and X-ray diffraction analyses are made to determine its microstructural and chemical composition. This article compares the efficiency of two RC models using a recently developed artificial intelligence technique, i.e., gene expression programming (GEP) and conventional technique, i.e., response surface method (RSM). Statistical models are developed to predict the CS, TS, EM, and D. The mathematical models are validated using determination coefficient ( R ² ) and adjusted coefficient ( R ² adj), and they are found to be significant. Furthermore, both methods (i.e., RSM and GEP) are very well correlated with the experimental data. The GEP is found to be more effective at predicting the experimental test results for RC. The projected methods can be executed for any practical value of RC.