Intrinsic psychosocial stressors and construction worker productivity: impact of employee age and industry experience

This paper aims to study the impact of employee age and industrial experience on intrinsic psychosocial stressors of construction workers. Using an integrated theoretical approach, this study examines the intrinsic (top management, career development, social support, motivation and work stress) psychosocial stressors that influence the productivity of Pakistani construction contracting firms workers having varied ages and industry experiences. Data were collected through a postal questionnaire survey. A comparative analysis of these data was undertaken for employees of varied ages and industrial experiences. Findings show that employees of varied ages did not concur over several top management, career development, social support, motivation and work stress related psychosocial stressors, whereas employees of varied industrial experience were in disagreement over some work stress related psychosocial stressors. Due to the need to overcome intrinsic psychological stresses, firm support is direly needed, especially for the less-experienced employees that are more susceptible to demotivation, mental stress and health and safety risks at the sites. The study provides valuable insights into worker productivity by showing how employee varied age and diverse industry experience are associated with the intrinsic psychosocial stressors that influence worker productivity. This study will help regulatory bodies to deal with the critical psychosocial stressors and devise such policies that improve the worker productivity of their construction contracting firms

Urban overheating assessment through prediction of surface temperatures: a case study of Karachi, Pakistan

Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI)

Exploring Managerial Perspectives of Using Building Management System through TAM: An Empirical Study of Commercial Sector of Pakistan

A cursory review of the Building Management System (BMS) which optimizes building performance as a move towards smart cities has been presented in the present study. The extant study is an effort to distinguish and analyze the circumstances as if the underdeveloped economies are less likely to be benefitted by the contemporary trends of BMS as compare to the developed countries. Moreover, the current study identifies the factors which may cause to render the managerial acceptance for using BMS through the Technology Acceptance Model (TAM). TAM was used to measure four behaviors (latent factors) namely subjective norms, organization support, compatibility, and technology complexity. The data were statistically evaluated via multiple regression analysis using the Statistical Package for Social Sciences (SPSS). Results suggested that organization support and compatibility have a significant influence on managerial intentions to use BMS while subjective norms, technology complexity have no significant influence. The findings of this study may serve as guidelines for improvement in the acceptance process and using building management systems in commercial sectors of developing countries

Water quality management using hybrid machine learning and data mining algorithms: An indexing approach

One of the key functions of global water resource management authorities is river water quality (WQ) assessment. A water quality index (WQI) is developed for water assessments considering numerous quality-related variables. WQI assessments typically take a long time and are prone to errors during sub-indices generation. This can be tackled through the latest machine learning (ML) techniques that are renowned for superior accuracy. In this study, water samples were taken from the wells in the study area (North Pakistan) to develop WQI prediction models. Four standalone algorithms, i.e., random trees (RT), random forest (RF), M5P, and reduced error pruning tree (REPT), were used in this study. In addition, 12 hybrid data-mining algorithms (combination of standalone, bagging (BA), cross-validation parameter selection (CVPS), and randomizable filtered classification (RFC)) were also used. Using the 10-fold cross-validation technique, the data were separated into two groups (70:30) for algorithm creation. Ten random input permutations were created using Pearson correlation coefficients to identify the best possible combination of datasets for improving the algorithm prediction. The variables with very low correlations performed poorly, whereas hybrid algorithms increased the prediction capability of numerous standalone algorithms. Hybrid RT-Artificial Neural Network (RT-ANN) with RMSE = 2.319, MAE = 2.248, NSE = 0.945 and PBIAS = -0.64, outperformed all other algorithms. Most algorithms overestimated WQI values except for BA-RF, RF, BA-REPT, REPT, RFC-M5P, RFC-REPT, and ANN- Adaptive Network-Based Fuzzy Inference System (ANFIS)

Error management climate and job stress in project-based organizations: an empirical evidence from Pakistani aircraft manufacturing industry

Drawing on the JD-R model, this study examines the influence of error management climate (EMC) on the job stress of frontline aeronautical employees. It also analyzes the moderating role of psychological capital (PsyCap) dimensions (i.e., hope, optimism, self-efficacy, and resilience) for the relationship between error management climate and job stress. The data was collected from 208 individuals through a questionnaire survey and was analyzed using a partial least squares structural equation modeling (PLS-SEM) approach. The results revealed that employees’ perceptions of error management climate have a significant negative impact on job stress. PsyCap optimism and PsyCap self-efficacy were found to have a negative moderating influence on the relationship between EMC and job stress. The other two dimensions of hope and resilience were found to have a moderating influence in the same direction as expected, but not at statistically significant levels. The findings of this study provide a unique perspective in realizing the part national and organizational cultures could play in either enhancing or attenuating the influence of an individual’s psychological resources such as psychological capital

Impact of Political, Social Safety, and Legal Risks and Host Country Attitude towards Foreigners on Project Performance of China Pakistan Economic Corridor (CPEC)

The China Pakistan Economic Corridor (CPEC) project was signed between China and Pakistan in the year 2013. This mega project connects the two countries to enhance their economic ties and give them access to international markets. The initial investment for the project was $46 billion with a tentative duration of fifteen years. Being an extensive project in terms of cost and duration, many factors and risks affect its performance. This study aims to investigate the effects of political (PR), social safety (SR), and legal risks (LR) on the project performance (PP) of the CPEC. It further investigates the significance of the host country’s attitude towards foreigners (HCA). A research framework consisting of PR, SR, and LR as independent variables, PP as the dependent variable, and HCA as moderator is formulated and tested in the current study. In this quantitative study, the Likert scale is used to measure the impact of the assessed risks. A questionnaire survey is used as a data collection tool to collect data and test the research framework and associated hypotheses. The partial least square structural equation modeling (PLS-SEM) is used to perform the empirical test for validation of the study, with a dataset of 99 responses. The empirical investigation finds a negative relationship between PR, SR, LR, and PP. It is concluded that PR, SR, and LR negatively influence the PP of CPEC. Furthermore, HCA negatively moderates the PR, LR, and PP of CPEC. In contrast, the value of SR and PP is positive in the presence of the positive HCA

Using multivariate regression and ANN models to predict properties of concrete cured under hot weather: a case of Rawalpindi Pakistan

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete