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The project selection criteria are required for an effective project lifecycle, and the selected projects should correspond with organizational aims and objectives; yet, the organization lacks severely in its ability to act decisively on the problem. Hence, this study aims to highlight the best project selection criteria that are related to organizational success. In this manner, a quantitative survey approach was used, with closed-ended questions distributed to employees in Pakistan's construction industry. The relationship between project selection criteria and organizational performance was evaluated using structural equation modelling in SmartPLS through measurement and structural models. The findings reveal that a significant positive correlation was found for all the project selection criteria, except for the financial criteria. Furthermore, the results demonstrate that project selection criteria are critical for organizational success; hence, the findings will assist organizations in making better project selection decisions.

The construction industry is going forward by adopting digitization, but players are still battling to grasp and execute it properly. That is why, this study aims to evaluate the awareness, challenges and opportunities of digitalization in the Malaysian construction industry and provides a framework for better digital implementation. In this perspective, a questionnaire-based study was conducted, and the responses were collected from field experts who have implemented a digital system in their construction projects. The outcome shows that the Malaysian construction industry is aware of digitalization and rated the Industrialized Building System (IBS) as the most important technology. In Project Delivery, “Standardized Organization” was the utmost factor for digitalization in construction. The top Challenge in Digital Implementation is “Organizational costs are among the high initial costs of implementing technologies, standards, and specifications”. The top Opportunity in Digital Transformation is “Procedures better planned and monitored”. Based on the top factors, the framework was constructed which will help the practitioners to understand and implement digitalization in a much better way. Thus, this study is significant in addressing the issues related to implementing digitalization in the Malaysian construction industry and providing a perspective to catalyse the development.

AbstractEarthquake risk in urban areas has recently taken quite a lot of attention. Risk is a cross-cutting combination of vulnerability and hazard. Abbottabad district, a rapidly growing district, is an earthquake-prone area in the north of Pakistan. In 2005, the region suffered enormous casualties due to a significant earthquake (Mw = 7.6). The various regional behaviours, including social, physical, and economic behaviours, are causing a vulnerable situation. At the same time, the high seismicity has the potential to invite earthquake hazard, thus ensuing a possible risk situation in the region. Consequently, this study proposes an integrated approach involving an artificial neural network (ANN), convolutional neural network (CNN), and analytic network process (ANP) to carry out the seismic risk analysis of the Abbottabad district. The proposed two integrated frameworks are ANP-ANN and ANP-CNN. This kind of methodology has not been applied before in this area. The geographic information system is used to formulate a broad range of contributing factors depicting the hazard and vulnerability of the area and potentially contributing to the seismic risk. A total of 16 contributing factors are chosen for this study. The selected factors are weighed using the ANP, and training and testing databases are generated to train and test the models, thus producing the area’s earthquake risk maps (ERMs). As per the ERMs, the northwestern regions and the borderline regions from the southeast, covering an area of 30%, possess a greater earthquake risk. The rest of the 70% of the area has less risk exposure. The AUC values of ANN and CNN are found to be 0.843 and 0.878, respectively, exhibiting a good performance by the models. The study paves the way for the concerned authorities to match and redefine their policies for developing, expanding, and managing the region’s structural, health care, and disaster management facilities.

As the global population raises, developing countries seem to have issues with the productivity of the construction projects due to high demand. Therefore, this paper aims to examine the effect of extrinsic diversity aspects impacting the construction worker efficiency. The data collection was made by a questionnaire sent to construction stakeholders working in the Punjab region of Pakistan. A comparison was drawn based on varied employee characteristics i.e., experiences and ages. It was found that old workers were negatively impacted by the distant location of the construction site. The young employees were most inclined towards holidays when working away from home. The access to basic amenities during a construction project was regarded as a major setback for the workers' productivity. Experienced workers preferred working within the vicinity and easy access to their houses. Based on the ethnicity factor while selecting the workforce, newcomers faced hurdles while entering the construction sector.

Over-budgeting due to inflation is a common phenomenon in the construction industry of both developed and developing countries. Inflation, with time changes, leaves an adverse effect on the project budget. Hence, this study aims to focus on the construction price index (CPI) behavior and inspect its correlation with inflation in Thailand’s construction industry as there has not been much work performed. The prediction of CPI was made from 2024 to 2028, relying on the data set from 2000 to 2023. The relationship between inflation and CPI categories helps in prediction by considering inflation as the independent variable and CPI (All Commodities, Lumber and Wood Products, Cement, and Iron Products) as the dependent variable that was incorporated in EViews to perform automated ARIMA forecasting. The correlation results show that out of four CPI, only Iron Products showed a significant relationship with inflation. For All Commodities, Lumber, and Wood Products, the predicted values were fluctuating, while for Cement and Iron Products, a clear seasonal pattern was observed. This prediction gives a direction to construction industry practitioners to make necessary adjustments to their budget estimation before signing the contract to overcome cost overrun obstruction.

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 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 (a 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).

This study presents a novel approach for forecasting the construction cost index (CCI) of building materials in developing countries. Such estimations are challenging due to the need for a longer time, the influence of inflation, and fluctuating project prices in developing countries. This study used three techniques—a modified Artificial Neural Network (ANN), time series, and linear regression—to predict and forecast the local building material CCI in Pakistan. The predicted CCI is based on materials, including bricks, steel, cement, sand, and gravel. In addition, the swish activation function was introduced to increase the accuracy of the associated algorithms. The results suggest that the ANN model has superior prediction results, with the lowest Mean Error (ME), Mean Absolute Error (MAE), and Theil’s U statistic (U-Stat) values of 0.04, 28.3, and 0.62, respectively. The time series and regression models have ME values of 0.22 and 0.3, MAE values of 30.07 and 28.3, and U-Stat values of 0.65 and 0.64, respectively. The proposed models can assist contractors, project managers, and owners through an accurately estimated cost index. Such accurate CCIs help correctly estimate project budgets based on building material prices to mitigate project risks, delays, and failures.

The healthcare industry significantly impacts the environment due to its high usage of energy and natural resources and the associated waste generation. This study applied a cradle-to-grave Life Cycle Sustainability Assessment (LCSA) approach to assess the environmental and social life cycles of public hospitals. One hundred twenty-four public hospitals were selected for the current study; their sustainability performance was compared with those certified by Leadership in Energy and Environmental Design (LEED). The comparison revealed several factors contributing to the poor sustainability performance of public hospitals. These include inadequate management, substandard planning, political interference, insufficient staffing and funding, high energy consumption, high expenses, inconsistent healthcare policies, and conventional building designs. System thinking was leveraged, and a causal loop diagram (CLD) was developed to visualize the interdependency of the identified indicators of LCSA. Based on the findings of the study, a policy framework is proposed to guide the development of sustainable healthcare buildings. The framework includes using eco-friendly materials and techniques in construction, harnessing solar energy, improving hospital management practices, promoting public awareness about sustainability, conserving energy and water, and adopting sustainable waste management and transportation. Additionally, it emphasizes addressing social issues such as improving indoor air quality, thermal comfort, lighting, acoustics, patient safety, and security and ensuring that healthcare services are accessible and affordable. This study contributes to the literature on sustainable healthcare buildings by providing a practical policy framework for achieving sustainability in the healthcare sector.

Soil erosion has serious threats to agricultural production, hydraulic structures, and the world’s ecosystem. The objective of this study is the delineation of soil erosion susceptibility zones in the Chitral district using spatial analyst tool in conjunction with Analytical Hierarchy Process (AHP). This district is highly vulnerable to soil erosion due to the mountainous topography. For the sustainability of agricultural land use as well as regional and local development, it is necessary to find out soil erosion probability zones and soil loss at the watershed scale. Eleven different factors; lithology, slope, elevation, plain curvature, lineaments, land cover, aspect, rainfall, drainage density, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) are considered in this study. Weights have been assigned to each factor, and maps have been generated through GIS (Geographic Information System) tools. The final map from the combination of all maps shows intensities of soil erosion in five different classes including very high, high, medium, low and very low. Very high and high erosion is observed in 13% and 18% of the total study area respectively, which shows that area is under serious danger of soil erosion. Elevation, slope, curvature, NDWI, and rainfall are found to be the dominant factors influencing soil erosion process. This study highlighted areas at risk of severe erosion which will be helpful for researchers and planners to plan for control of soil erosion.

The COVID-19 outbreak is changing the patterns in travel activity for key destinations.Travel behavior during the pandemic has not been investigated adequately, specifically in developing countries. A sound understanding of travel-mode choice determinants is needed to design interventions to slow down and prevent the spread of the COVID-19 disease. This study explores travel-mode choice determinants for three key destinations, the workplace, market, and hospital, in Islamabad, Pakistan, during the COVID-19 disease outbreak. This study used a primary dataset of 163 observations and applied the multinomial logit (MNL) regression to analyze it. The survey results highlighted that the proportion of public transport mode was marginal for the three key destinations because public transport was closed during the lockdown, except for the metro bus. The streamlined model estimation results implied that the family-size factor had no relationship with the travel-mode choice. Males were most likely to travel to the workplace and market by 2&3 wheelers and least likely to travel by car. Females, unemployedpersons, and students are likely to stay at home. Married people were more likely than single people to travel to the workplace and hospital by car. Self-employed people and state officials/public servants were most likely to go to the market by car. People living in towns/rural areas and cities were likely to travel by motorcycle/rickshaw and car, respectively. People living farther than 5 km from the workplace were most likely to travel by car, followed by motorcycle. This study is important for designing strategies to curb the pandemic with sustainable mobility during the lockdown.

Labours in construction are one of the main pillars in the construction industry of Malaysia for projects execution. Construction labours not only contributes to the development of the construction industry but also impacts the Malaysian economy. Consideration of labour wages is made in the initial phase of the project budget, however, wages are getting changed over time. The inflation rate is one of the key factors which affect labours wages. Regrettably, the inflation rate is being ignored while computing labour wages for projects budget development, resulting in cost overrun of construction projects. In this regard, the correlation coefficient test was used to determine the impact of the inflation rate on labour wages gathered from the year 2013 to 2019. The results showed that a significant acceptable relationship exists among the inflation rate and several categories of labour wages. Most of the labour wages showed a negative relationship with the inflation rate, indicating the deviation in the wages, thus, result in cost overrun. To steer the cost overrun effect, it is recommended to adopt automation system and introduce the Industrial Revolution (IR) 4.0 in construction projects as a replacer of labours.

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.

Abstract During the last decade, 2.4 billion individuals, the majority of which were residing in low economic growth countries, became victim of global disasters. As economic growth is correlated with the resilience of a nation against a disaster, therefore developing and underdeveloped nations significantly face higher magnitude of losses. With Pakistan being a developing country, the present study takes into account the most recently accomplished mega project of METROBUS service that is currently operational in the twin metropolitan cities of Pakistan i.e. Islamabad and Rawalpindi to study the pre and post development perspectives of project in terms of the hazard perception, prospect disaster threats it poses towards the society in case of major seismic activity and the magnitude of disaster preparedness associated to the potential hazardous circumstances as result of seismic event occurrence. Statistical evaluations for the current study were conducted utilizing the SmartPLS 3.0, being based upon the information collected through 336 self-supervised surveyed responses. The results of the current study indicated, the respondents to be highly in favor of the commute service project, but at the same time, they highlighted the concerns regarding socio-economic adversities faced during the project development phases as well as after its completion. Further, the respondents expressed major reservations regarding the disaster management measures taken by the potential stakeholders of the project. Despite the fact that most of the respondents were well educated, the lack of awareness regarding preparedness against any possible project related disastrous situation, was observed. To mitigate the potential disaster losses associated to the project, respondents suggested that the national authorities should proactively provide awareness to the public and assure the availability of all necessary facilities, in case of any hazardous situation; seismic in particular. Considering the practical implications, the current study may be useful for other developing countries, which too faced similar concerns during development of comparable megaprojects and share similar environmental conditions.

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.

Mixed Reality (MR) that combines elements of both augmented reality (AR) and virtual reality (VR) has great potential for use in the construction industry. However, its usage in construction projects in developing countries has not been widely researched. This study aims to examine the major drivers of, and barriers to, the adoption of MR technologies (MRTs) in the construction sector of developing countries. A mixed methodology that included both qualitative and quantitative data analysis was used. The literature review revealed 37 barriers to, and 41 drivers of, MR adoption. A questionnaire was then distributed to 220 randomly selected respondents from the pertinent construction industry, representing all major stakeholders. The relative importance index (RII) was used to rank the barriers and drivers in terms of significance. The results showed that the primary barriers to MR adoption are the high cost of initial investment, public perception of the technology being immature, limited demand, and difficulty accessing relevant experts’ knowledge. The key drivers of MR adoption include improved project knowledge, reduced overall project costs, low-cost and realistic training scenarios, reduced damage and development costs, and enhanced user experience. These findings provide insights into the major barriers and drivers of MR in the construction sector of developing countries and will help pertinent companies to focus their research and development (R&D) efforts on overcoming these barriers and promote their adoption to move towards the much sought-after construction automation and digitalization.

Nurses make up most of the global healthcare system, thus justifying their significance in the respective industry. The healthcare profession is amongst the very few careers that are attributed to a very high level of stress and imbalanced work-life equilibrium. Over past decades, the said nature of work has been observed to coerce countless nurses to leave their respective organizations. Considering this, the current study primarily evaluates the impact of role stressors on the turnover intentions of nurses employed in the healthcare industry of Pakistan. Secondly, the study examines the mediatory role of organizational cynicism between the role stressors and turnover intentions to explain the escalating trend of nurses intending to leave the industry. Lastly, it assesses the moderating role of self-efficacy between the organizational cynicism and the intended turnover of a nurse, to gauge the amount of variation self-efficacy can cause to mitigate the negative attitudes of employed individuals. The current study was conducted in the twin metropolitans of Pakistan i.e., Rawalpindi and Islamabad. A total of 394 responses were statistically evaluated using SmartPLS 3.0. The results of the current study indicated a significant impact of role stressors on the turnover intentions of nurses. Also, organizational cynicism was observed as a significant mediator between role stressors and turnover intentions. Further, self-efficacy as well was observed as a significant moderator between organizational cynicism and the intent of healthcare workers to leave the organization. The present study addressed the conceptual research gap by exploring the direct cumulative effect of role stress (role ambiguity, role conflict, role stressors, work-family conflict) in developing the intents of professionals to quit their employing organizations, the mediatory role of organizational cynicism, and the moderation effect of self-efficacy between the undertaken variables. While in terms of abridging the contextual research gap, the current study evaluated the proposed research model within the healthcare sector of Pakistan. The findings of the current study commended the management personnel of the healthcare industry to provide the nurses with healthy professional environments to work in, as well as catering to their professional and personal expectations to a better extent. Hence, increasing the ownership of an individual depicted towards his/her employing organization.

The international services literature lacks research investigating the association between corporate size and corporate international experience with the internationalization. Using an integrated theoretical approach, this paper examines the internationalization of construction service corporations (CSCs) with specific emphasis on four dimensions: motives, firm-specific competitive advantages, home country-specific competitive advantages and locational considerations. A quantitative approach was adopted through a postal questionnaire survey. Out of 126 CSCS, 84 usable responses were analyzed giving 66.7% response rate. A comparative analysis was conducted using Mann-Whitney U test between the corporations having different sizes and international experiences. The findings show that CSCs of varied sizes did not concur over several firm-specific competitive advantages and home country-specific competitive advantages, whereas CSCs of varied international experiences were in disagreement over some locational factors. Small CSCs need to form joint ventures or partnerships in order to overcome their limitations. The government support is direly needed to promote internationalization of CSCs especially the younger and smaller ones which stand more vulnerable due to lack of opportunities in their domestic markets. The study will be helpful to managers and regulatory bodies towards creating a balanced playing field to develop the internationalization process for their service corporations.

Due to the increased globalization and the disruptions caused by pandemics, supply chains (SCs) are becoming more complex in all industries. Such increased complexities of the SCs bring in more risks. The construction industry is no exception; its SC has been disrupted in line with its industrial counterparts. Therefore, it is important to manage the complexities in integrating SC risks and resilient capabilities (RCs) to enable a resilient SC in construction. This study investigated the complexity involved in the dynamics of effects between organizations’ SC risks and RCs to overcome disruptive events. Past researchers investigated how to improve the performance of construction projects, regardless of the complexities and interdependencies associated with the risks across the entire SC. However, the system dynamics (SD) approach to describe the diversity of construction SCs under risks has received limited attention indicating a research gap pursued by this study. This work aimed to analyze and establish interconnectivity and functionality amongst the construction SC risks and RCs using systems thinking (ST) and SD modeling approach. The SD technique is used to assess the complexity and integrated effect of SC risks on construction projects to enhance their resilience. The risks and RCs were identified by critically scrutinizing the literature and were then ranked through content analysis. Questionnaire surveys and expert opinions (involving 10 experts) helped develop causal loop diagrams (CLDs) and SD models with simulations to assess complexity qualitatively and quantitatively within the system. Research reveals that construction organizations are more vulnerable to health pandemics, budget overruns, poor information coordination, insufficient management oversight, and error visibility to stakeholders. Further, the most effective RCs include assets visibility, collaborative information exchange, business intelligence gatherings, alternative suppliers, and inventory management systems. This research helps industry practitioners identify and plan for various risks and RCs within their organizations and SCs. Furthermore, it helps understand trade-offs between suitable RCs to abate essential risks and develop preparedness against disruptions to improve organizational policymaking, project efficiency, and performance.

The study is based on validating and exploring the effects of a mega project plan (CPEC) on infrastructure development and Sustainable Project Management. The CPEC has great importance to infrastructure development and economy-boosting. The current study's primary aim is to deal with environmental protection, economic boost up, international relations influencing to the Project's success. The paper also addressed project management as a moderator between environmental protection, economic boost up, international relations, and the CPEC project's success. The primary data has been gathered by using questionnaires, and PLS-SEM has been employed for the analysis. The results revealed that environmental protection, economy boost up, and international relations have a positive association with the success of CPEC. The outcomes also exposed that project management moderating among the nexus of economy boosts up the international relations and success of CPEC. The present study results guided how Pakistan and China make the CPEC project stronger with the efficient implementation of practices required for protecting the environment, with the economic growth and boost up, and good strong relations with foreign countries. This study was an attempt to validate the different factors to check their association with each other in a new environment, resulting in a leading edge for the success of mega projects that influence project management.

Disaster is a state of serious disruptions in the functionality of any society or county. Disasters pose serious economic or environmental impacts that surpass the capacity of the affected country or society to compete with the use of their assets. Recently, Pakistan significantly prone to health disasters due to COVID-19 among developing South Asian countries. The long-term impact of health disasters and other natural hazards put additional pressure mostly on the government's economic policy. It forces the government to follow a constructive approach like a disaster relief-based approach rather than a conventional mitigation management formation to reduce the impact of disaster risk. This study elaborates on the main issues associated with disaster preparedness as well as recovery of the economy and businesses of the country. For Scientific risk performance analysis, open-source data from the National Institute of Disaster Management (NDMA) has been utilized to study the current situation of COVID-19 in Pakistan. Results show Pakistan has been facing a highly vulnerable situation as more than three hundred and fifty thousand confirmed cases have been reported. Poor health and technical management facilities have been exposed against COVID-19 as Pakistan has a low heath budget because of its declining GDP growth rate in the world. This research will help in disaster preparedness and the development of a disaster risk management framework while designing strategies to deal with such pandemics in the future.

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.

Disposal of municipal solid waste (MSW) is one of the significant global issues that is more evident in developing nations. One of the key methods for disposing of the MSW is locating, assessing, and planning for landfill sites. Faisalabad is one of the largest industrial cities in Pakistan. It has many sustainability challenges and planning problems, including MSW management. This study uses Faisalabad as a case study area and humbly attempts to provide a framework for identifying and ranking landfill sites and addressing MSW concerns in Faisalabad. This method can be extended and applied to similar industrial cities. The landfill sites were identified using remote sensing (RS) and geographic information system (GIS). Multiple datasets, including normalized difference vegetation, water, and built-up areas indices (NDVI, NDWI, and NDBI) and physical factors including water bodies, roads, and the population that influence the landfill site selection were used to identify, rank, and select the most suitable site. The target area was distributed into 9 Thiessen polygons and ranked based on their favorability for the development and expansion of landfill sites. 70% of the area was favorable for developing and expanding landfill sites, whereas 30% was deemed unsuitable. Polygon 6, having more vegetation, a smaller population, and built-up areas was declared the best region for developing landfill sites and expansion as per rank mean indices and standard deviation (SD) of RS and vector data. The current study provides a reliable integrated mechanism based on GIS and RS that can be implemented in similar study areas and expanded to other developing countries. Accordingly, urban planning and city management can be improved, and MSW can be managed with dexterity.

Over the past several years, global project management teams have been facing dynamic challenges that continue to grow exponentially with the increasing number of complexities associated with the undertaken tasks. The ever-evolving organizational challenges demand project managers to adapt novel management practices to accomplish organizational goals rather than following traditional management practices. Considering which, the current study aims to explain the effect of agile management practices upon project performance directly as well as while being mediated through project complexity. Furthermore, the aforementioned mediatory relationship is evaluated in terms of the moderating effect of leadership competencies. The current study utilized the survey approach to collect the data from registered I.T firms deployed in the potential metropolitans of each province of Pakistan including, Peshawar, Islamabad, Lahore, Sialkot, Faisalabad, Hyderabad, Sukkur, and Karachi. A total of 176 responses were utilized for statistical evaluations. As result, it was observed that the negative influence anticipated by project complexity on project performance was compensated by the agile management practices. Further, the leadership competencies played a pivotal role in managing project complexity while implementing agile management practices and therefore enhancing project performance. The current study abridges the potential knowledge gap conceptually by evaluating the direct impact of agile management upon project performance while considering all of its aspects, exploring the mediatory role of project performance and evaluating the moderating role of leadership competencies in attaining optimum project performance. In contextual terms, the current study fills the knowledge gap by gauging the implications of agile management practices within the I.T sector of Pakistan. The results of the current study can be a potential guide for both the academicians and the industry professionals.

This work evaluates the performance of three machine learning (ML) techniques, namely logistic regression (LGR), linear regression (LR), and support vector machines (SVM), and two multi-criteria decision-making (MCDM) techniques, namely analytical hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), for mapping landslide susceptibility in the Chitral district, northern Pakistan. Moreover, we create landslide inventory maps from LANDSAT-8 satellite images through the change vector analysis (CVA) change detection method. The change detection yields more than 500 landslide spots. After some manual post-processing correction, the landslide inventory spots are randomly split into two sets with a 70/30 ratio for training and validating the performance of the ML techniques. Sixteen topographical, hydrological, and geological landslide-related factors of the study area are prepared as GIS layers. They are used to produce landslide susceptibility maps (LSMs) with weighted overlay techniques using different weights of landslide-related factors. The accuracy assessment shows that the ML techniques outperform the MCDM methods, while SVM yields the highest accuracy of 88% for the resulting LSM.

Manual inspection of infrastructure damages such as building cracks is difficult due to the objectivity and reliability of assessment and high demands of time and costs. This can be automated using unmanned aerial vehicles (UAVs) for aerial imagery of damages. Numerous computer vision-based approaches have been applied to address the limitations of crack detection but they have their limitations that can be overcome by using various hybrid approaches based on artificial intelligence (AI) and machine learning (ML) techniques. The convolutional neural networks (CNNs), an application of the deep learning (DL) method, display remarkable potential for automatically detecting image features such as damages and are less sensitive to image noise. A modified deep hierarchical CNN architecture has been used in this study for crack detection and damage assessment in civil infrastructures. The proposed architecture is based on 16 convolution layers and a cycle generative adversarial network (CycleGAN). For this study, the crack images were collected using UAVs and open-source images of mid to high rise buildings (five stories and above) constructed during 2000 in Sydney, Australia. Conventionally, a CNN network only utilizes the last layer of convolution. However, our proposed network is based on the utility of multiple layers. Another important component of the proposed CNN architecture is the application of guided filtering (GF) and conditional random fields (CRFs) to refine the predicted outputs to get reliable results. Benchmarking data (600 images) of Sydney-based buildings damages was used to test the proposed architecture. The proposed deep hierarchical CNN architecture produced superior performance when evaluated using five methods: GF method, Baseline (BN) method, Deep-Crack BN, Deep-Crack GF, and SegNet. Overall, the GF method outperformed all other methods as indicated by the global accuracy (0.990), class average accuracy (0.939), mean intersection of the union overall classes (IoU) (0.879), precision (0.838), recall (0.879), and F-score (0.8581) values. Overall, the proposed CNN architecture provides the advantages of reduced noise, highly integrated supervision of features, adequate learning, and aggregation of both multi-scale and multilevel features during the training procedure along with the refinement of the overall output predictions.

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks.

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).

This paper examines how the latest mega plan and the Belt and Road Initiative (BRI) will impact the geopolitics of energy and infrastructural development. With a massive change in the supply and demand of global energy and its infrastructure, the transition of international energy order is in the making. While the US is going towards a more isolationist path from its traditional superpower role, there are rising economies such as China, India, Japan, and Russia which are undoubtedly playing a vital role in the geopolitical stage and the other development endeavors. Several regions such as Central Asia, the Arctic, Eastern Mediterranean, and the South China Sea are offering substantial natural gas and oil reserves and drawing global attention to develop energy cooperation. This situation is profoundly influencing the transition of energy order. In this transition, BRI is supposed to play an important role. As a mega development strategy with a robust geostrategic dimension, it purposes to advocate interconnectivity collaboration in framework, exchange, and advancement among the partaking nations. This super arrangement offers a lot of ventures, foundation developments, and modern reconciliation in the energy sector. The country is trying to establish a multilateral platform for endorsing and shielding energy cooperation under BRI. This paper, therefore, attempts to observe how this mega plan will contribute to reshaping the existing energy order as well as the geopolitics of energy with motivation on multifaceted energy collaboration.

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.

Land degradation caused by soil erosion is considered among the most severe problems of the 21stcentury. It poses serious threats to soil fertility, food availability, human health, and the world ecosystem. The purpose of the study is to make a quantitative mapping of soil loss in the Chitral district, Pakistan. For the estimation of soil loss in the study area, the Revised Universal Soil Loss Equation (RUSLE) model was used in combination with Remote Sensing (RS) and Geographic Information System (GIS). Topographical features of the study area show that the area is more vulnerable to soil loss, having the highest average annual soil loss of 78 ton/ha/year. Maps generated in the study show that the area has the highest sediment yield of 258 tons/ha/year and higher average annual soil loss of 450 tons/ha/year. The very high severity class represents 8%, 16% under high, 21% under moderate, 12% under low, and 13% under very low soil loss in the Chitral district. The above study is helpful to researchers and planners for better planning to control the loss of soil in the high severity zones. Plantation of trees and structures should be built like check dams, which effectively control the soil erosion process.