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The construction industry is slowly embracing digitalisation in line with the Industry 4.0 revolution and the aftermath of the COVID-19 pandemic. However, progress has been sluggish due to stakeholders’ limited awareness of digital skills. This study addresses this issue by developing a comprehensive taxonomy of digital skills required to successfully implement the Industry 4.0 principles of digitalisation in the construction industry. A systematic literature review was conducted by mining the Scopus and Web of Science databases to identify relevant literature and map the skills currently used or needed for digitalisation. The study also examined publication trends and outlets to gain insight into developments. Additionally, VOSviewer was used to conduct a scientometric analysis of the shortlisted articles to identify important keywords and authorship collaboration networks within this research domain. A total of thirty-five digital skills were identified from the literature. These skills were organised into a taxonomy with categories named automation and robotics, coding and programming, design, drafting and engineering, digital data acquisition and integration, digital literacy, digitisation and virtualisation, modelling and simulation, and planning and estimation. The developed taxonomy will help stakeholders plan strategically to provide digital skills to the new graduates joining the workforce, enabling a more comprehensive approach to the digitalisation of the construction industry.

Construction organizations are challenged with effective management of their human resource (HR) as both are integral to each other. But the state of practice in the developing countries is not encouraging and construction professionals found themselves less motivated and enthusiastic about their job. This pushes to study the motivational needs of construction professionals of different career levels. Therefore, the current study benchmarks the HR practices of the construction industry of Pakistan to measure if there is any difference between what professionals truly value and what they get. In doing so, data was collected from 250 professionals through a questionnaire survey. Results reveal that most of the motivational needs are not gratified and the only stakeholder doing better are clients who’s mid- and end-career professionals feel somewhat satisfied. Practical suggestions are provided to help the construction industry upgrade its HR vision and facilitate its most precious resource.

Real Estate Online Platforms (REOPs) are used for conveying real estate and property-related information to potential users (buyers, renters, or sellers). The information leveraged through REOPs supports these users in reaching conclusive rent or buy decisions. Despite their promised utility, user perception about accepting online information through REOPs is unexplored. Using a comprehensive questionnaire and data collected from 65 users, the current study captures the users’ perception of REOPs. Risk, service, information, system, technology adoption model (RSISTAM) is proposed comprising of seven users’ perceptions: risk (PR), service quality (PSEQ), information quality (PIQ), and system quality (PSYQ) from the information systems success model, and usefulness (PU), ease of use (PEU) and behaviour to accept (BAU) from TAM. The results are analysed using the decision making trial and evaluation laboratory (DEMATEL) approach, which shows that PIQ, PSEQ and PEU are the causes and PR, PSYQ, PU and BAU are the effects. Among the criteria, the order of prominence is PEU > PSEQ > PIQ, and for net effects, the order is PU > BAU > PSYQ > PR. For addressing the causes, the REOP managers must provide more transparent, high quality and voluminous information to the users, focus on the system, services, and information qualities, and add more enjoyable, immersive and easy-to-use content through REOPs. This study contributes to the body of knowledge by exploring user perceptions and proposing methods to improve the quality and reliability of REOPs in line with Real Estate 4.0 and industry 4.0 aims.

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.

Building information modeling (BIM) through data-rich digital representation has revolutionized the architecture, engineering, and construction (AEC) industry. BIM implementation in the AEC industry has noticeably increased over the last decade. Various BIM roles have been discussed in the literature to ease the process of BIM implementation, but the BIM roles related to project delivery methods have not been standardized. Stimulated by this need, this study develops a BIM roles and responsibilities matrix (BIM-R&R) in the context of the design−bid−build (DBB) projects for developing countries. A comprehensive literature review has been conducted, followed by a questionnaire survey comprising 105 responses. The results were analyzed to formulate a BIM-R&R matrix, on which the expert opinion was obtained from the BIM experts. The proposed BIM-R&R matrix describes all the roles and their corresponding responsibilities required along the project life cycle phases of DBB projects. The incorporation of BIM roles in the DBB procurement process will aid in the efficient management of all information and data that may be lost due to the fragmented nature of DBB. BIM roles with enhanced communication and coordination will also help in reducing time and cost overruns while maintaining a high-quality product. This study helps the associated construction industry in its efforts to implement BIM on their projects by providing a method by which to assess which BIM roles are necessary. Moreover, it will provide project and construction managers with a clear understanding of the BIM roles in DBB projects.

This study addressed the complexity involved in integrating the causative risk factors influencing construction profitability. Most of the existing studies cover the individual effects of profitability influencing factors. Very few focus on the systematic impact without incorporating the complexity and associated dynamics, presenting a gap targeted by the current study. The current study aimed to assess causative interrelations and interdependencies between profitability influencing risk factors (PIRF), through systems thinking (ST) and system dynamics (SD) modeling. The SD approach was used to evaluate the integrated impacts on profitability-influencing risk categories (PIRC) in construction projects. The causative influencing factors affecting construction profitability were identified through a comprehensive literature review. These were ranked using content analysis, and categorized into significant issues. Through 250 structured surveys and 15 expert opinion meetings, the path for quantitative and qualitative evaluations was prepared. Following these investigations, a causal loop diagram (CLD) was established using the ST technique, and the integrated effect was quantified using SD modeling. The study finds the rising cost of material, supply chain process, payment issues, planning and scheduling problems, financial difficulties, and effective control of manpower and equipment resources as the most critical PIRFs. The integrated effects of PIRFs on PIRC were quantified using SD modeling. This study helps field professionals with profitability-influencing factors, diagnosing issues, and integrating impacts regarding decision-making and policy formulation. For researchers, it presents a list of factors that can be investigated in detail, and the holistic interrelationships established.

Building materials with a low environmental impact are critical to the sustainability of the built environment. The environmental impact of materials can be determined by a Life Cycle Impact Assessment (LCIA), which constitutes multiple parameters such as the water used in a material’s life cycle. To use the LCIA approach for building material selection, its parameters need to be assigned different weights, which is the primary objective of this study. Building Information Modelling (BIM) can play an influential role when using LCIA during the building design process. With this consideration, we study the attention given to environmental sustainability in buildings and the responsiveness of BIM in this case. A multi-regional survey of 120 experts from academia and industry was conducted. The results show the relative importance of LCIA parameters and the focus of the building sector on environmental sustainability. The current and the future responsiveness of BIM towards environmental sustainability is also indicated. To promote the integration of LCIA in building design and performance assessment, the future role of BIM applications is explored. The results will contribute to research and practice in the sustainable built environment by helping select environment-friendly building materials.

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.

Cities in Pakistan, because of rapid pace of urbanization, are unable to provide adequate housing supply to cater to the needs of the people. Islamabad, the capital of Pakistan, has a population of two million with a growth rate of six percent. Construction of apartment buildings, as one of the alternatives to fulfil the housing needs, is on the rise. However, apartments are being designed without considering the needs of the residents. This research focuses on assessment of socio-economic profile and satisfaction of residents of single unit houses vis-à-vis of those living in apartments. Fried and Gleicher’s approach was used in this research. This study analysed the socioeconomic characteristics, level of satisfaction of residents; drivers and barriers that influence the residential satisfaction of residents in two types of residential units in Islamabad. This study finds that residents of both housing types were equally satisfied with housing attributes and surrounding neighborhood while the residents of single unit houses were more satisfied with facilities, maintenance and culture. This study suggests that more focus should be given to factors like open space, basic amenities, privacy while designing an apartment building.

Rising demand and limited production of electricity are instrumental in spreading the awareness of cautious energy use, leading to the global demand for energy-efficient buildings. This compels the construction industry to smartly design and effectively construct these buildings to ensure energy performance as per design expectations. However, the research tells a different tale: energy-efficient buildings have performance issues. Among several reasons behind the energy performance gap, occupant behavior is critical. The occupant behavior is dynamic and changes over time under formal and informal influences, but the traditional energy simulation programs assume it as static throughout the occupancy. Effective behavioral interventions can lead to optimized energy use. To find out the energy-saving potential based on simulated modified behavior, this study gathers primary building and occupant data from three energy-efficient office buildings in major cities of Pakistan and categorizes the occupants into high, medium, and low energy consumers. Additionally, agent-based modeling simulates the change in occupant behavior under the direct and indirect interventions over a three-year period. Finally, energy savings are quantified to highlight a 25.4% potential over the simulation period. This is a unique attempt at quantifying the potential impact on energy usage due to behavior modification which will help facility managers to plan and execute necessary interventions and software experts to develop effective tools to model the dynamic usage behavior. This will also help policymakers in devising subtle but effective behavior training strategies to reduce energy usage. Such behavioral retrofitting comes at a much lower cost than the physical or technological retrofit options to achieve the same purpose and this study establishes the foundation for it.