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The construction industry is significant in the economic growth of developing countries and is recognized globally for its high potential in sustainable development. Offsite Modular Construction (OMC) has the potential to revolutionize this sector. However, the adoption of OMC in developing nations remains limited due to various barriers. This study fills a critical gap by developing a novel framework that integrates the analysis of Offsite Modular Adoption Barriers (OMABs), i.e. resource availability, process management, and issues/perceptions, with the pursuit of overall sustainable future needs (OSFN) in residential projects. For this study, 314 construction specialists in Malaysia, a developing country with high construction activity, were surveyed. The study further employs PLS-SEM for latent variable relationship identification between OMABs and OSFN, which has not been researched comprehensively in the literature. The study reveals that there is a moderate positive correlation between OMABs and 33.6 % of the sustainable outcomes in residential projects. The path coefficient (β = 0.316) also provides evidence of the importance of eliminating these barriers in improving sustainability goals. This research is innovative in its approach as it seeks to address both the issues of OMC and sustainability in the developing nations. It is highly beneficial for policymakers as it provides a clear guideline on how to improve the OMC usage and promote sustainable construction. The SEM model can be used as a reference for other developing economies that are interested in the modernization of the construction industry for sustainable development.

Construction monitoring in dynamic construction site environments poses significant challenges for construction management. To overcome these challenges, the implementation of computer vision (CV) technologies for construction project monitoring has gained traction. This study focuses on investigating the factors influence the successful implementation of CV technologies in monitoring construction activities within building projects. A comprehensive methodology was employed, including a systematic review of CV technologies implemented in construction and qualitative surveys conducted with construction experts. Additionally, a quantitative questionnaire was developed, and the collected data was analysed using structural equation modelling. The findings reveal the presence of 10 factors categorized into four constructs. Notably, all 10 factors demonstrate high value factor loadings and statistical significance, and among the four constructs (device, jobsite, environment, human), device (0.82) has the highest impact on the implementation of CV-based technologies on the construction site, followed by jobsite condition (0.62), human (0.61), and environment (0.51) came in the last place. By addressing these influential factors and mitigating their effects, construction stakeholders can enhance the implementation of CV technologies for monitoring construction sites. This study contributes valuable insights that inform the implementation and optimization of CV technologies in construction projects, ultimately advancing the field of construction management.

Bridges are generally acknowledged as one of the vital structures of transportation systems. Meanwhile, they are prone to time-variant damage and deterioration mechanisms over their life span. With that in mind, this research study aims to explore state-of-the-art work in relation to deterioration models and related critical factors of reinforced concrete bridges. Particularly, this study presents a mixed review methodology (scientometric and systematic) that reviews over 300 publications in Scopus and Web of Science databases over the period 1985–2023. The study scrutinized and categorized the wide spectrum of deterioration factors in reinforced concrete bridges with the help of deterioration models. Results manifested that implicating deterioration factors can be grouped into seven main clusters, namely chemical, material properties, design & construction, physical, operational, environmental, and force majeure. In addition, it is noted that hitherto, there has been a lack of sufficient research efforts on non-destructive evaluation-based deterioration models.

Cement-based composites (CBCs) are essential in the construction sector due to their cost-effectiveness, availability, and versatility, but they struggle with low tensile strength and poor heat resistance. Recent advancements have highlighted the potential of nanomaterials, particularly graphene oxide (GO), in enhancing the mechanical, thermal, and electrical properties of CBCs. This study aims to provide a comprehensive review of the incorporation of GO into cementitious composites, examining its impact on microstructure, mechanical properties, rheology, and durability; thus, a bibliometric review and scientometric analysis were conducted to thoroughly evaluate the existing literature. A total of 263 studies were selected for thorough study. It can be concluded that GO content acts as a pore filler, decreasing porosity by 23% and average pore size by 22%, while boosting compressive strength by up to 15% at a 0.05% concentration. It also enhances workability, stability, and resistance to chloride ingress, sulfate attack, alkali–silica reaction, and carbonation. Incorporating GO reduces cement consumption and carbon footprint, leading to more durable structures and supporting sustainable construction by efficiently utilizing waste materials. The optimal GO concentration for these benefits ranges from 0.03% to 0.1% by weight of cement, as higher concentrations may cause agglomeration. GO-modified cementitious materials are well suited for high-performance and durable applications, particularly in environments with chemical and mechanical stresses.

The transformational potential of 3D printing within the manufacturing industry has been widely recognized. However, its widespread adoption faces significant practical challenges that extend beyond the technology itself, encompassing political, economic, social, and technical (PEST) factors. This research investigates the external variables impacting the successful implementation of 3D printing and proposes strategies to overcome these barriers. Through exploratory factor analysis (EFA) and structural equation modeling (SEM), we identified key external factors, including governmental regulations, economic conditions, societal acceptance, and technical readiness, that significantly influence the success of 3D printing deployment. Notably, addressing technological barriers emerged as a crucial factor. To address these implementation challenges, we recommend increased public awareness and stakeholder participation. Our findings indicate that these strategies can enhance the prospects of 3D printing adoption. These results hold significant implications for policymakers, practitioners, and researchers in the 3D printing industry. Understanding and mitigating external obstacles are critical for harnessing the benefits of 3D printing within a dynamic environment. This research sheds light on the multifaceted challenges surrounding 3D printing implementation, emphasizing the importance of proactive measures to overcome these hurdles.

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.

Blockchain technology provides a decentralized way of handling the data and supports the construction project's sustainability and performance. Small construction projects in Malaysia always need help adopting new technology like blockchain. This research aimed to investigate the challenges affecting the implementation of blockchain technology in small construction projects in Malaysia. The study examined Malaysian minor construction project blockchain implementation issues. A total of 17 challenges were found significant in affecting the implementation of Blockchain technology in small construction projects in Malaysia. Five constructs were seen: technological barriers, work environment barriers, economic and planning barriers, operational barriers, and privacy and regulation barriers. Structural equation modeling and these structures create a blockchain implementation model for modest building projects. The model's validity, dependability, and forecast accuracy for all obstacles and their potential influence on blockchain deployment in small construction projects in Malaysia are substantial. The model also has good convergent and discriminant validity, supporting it. Blockchain use in Malaysian small building projects could be improved mainly through economic and planning issues. The present study needs to highlight the Malaysian small construction sector's essential obstacles in integrating blocks and technology that are the basis for more creative technologies. This research will help the Malaysian construction sector concentrate on improvements and assess the effect of blockchain on modest projects.

While pivotal for economic growth, the global construction sector confronts critical challenges due to its resource-intensive practices and environmental impact. Leadership within this industry becomes essential as the imperative for sustainable development gains prominence. This study addresses a significant knowledge gap by investigating the nexus between sustainable leadership practices and the construction sector, focusing on Pakistan. A comprehensive framework is developed by combining literature review, expert analysis, and robust statistical methods. Data collection involved questionnaire involving 206 participants from construction industry professionals in Pakistan. The study examines the relation of green building design, certification standards, life cycle assessment, renewable energy integration, resilient infrastructure, social equity and inclusion, and waste management with sustainable leadership practices. The findings affirm the positive and significant relationships between these dimensions and sustainable leadership. The study contributes to academia and industry, offering a novel understanding of how sustainable leadership can drive resilient and eco-conscious practices in construction. These insights hold implications for shaping policies, fostering industry transformation, and creating a sustainable and resilient built environment for the future.

The implementation of 3D laser scanning technology within the architecture, engineering, and construction (AEC) sector possesses the capability to enhance project efficacy and precision. Nevertheless, there exist various obstacles that impede their extensive adoption. This study investigates the key challenges affecting their adoption in the industry. The study employed a mixed-method methodology that involved exploratory factor analysis (EFA) and structural equation modeling (SEM) to examine survey data collected from professionals operating within the architecture, engineering, and construction (AEC) industry. The results reveal significant technical, operational, awareness, and economic challenges. Technical challenges include data processing complexity, limited resources, and compatibility issues. The integration of laser scanning data and perceived risks present operational challenges. Awareness challenges relate to legal factors and change aversion. Economic challenges encompass affordability and time-consuming data acquisition. The ramifications for the industry are significant. Managers should invest in resources, training programs, awareness campaigns, and cost-effective solutions. Future research should expand the sample size and examine long-term effects. Addressing these challenges will enable the AEC industry to fully leverage the potential of 3D laser scanners, enhancing project outcomes and efficiency in the built environment.

This study examines the obstacles that impede the implementation of cloud computing (CC) in small-scale building projects. Based on a comprehensive analysis of relevant scholarly sources, this research paper aims to delineate the many obstacles that are often encountered during the implementation of CC. A comprehensive analysis is conducted based on the replies obtained from a survey of 217 stakeholders in the building sector. Using an exploratory factor analysis (EFA) methodology, this research assesses the interrelated obstacles associated with technological and operational restrictions, risk and privacy considerations, cultural dynamics, behavioural variables, unpredictability, and resource barriers. Furthermore, a model is developed using Structural Equation Modelling (SEM) in order to get a deeper comprehension of these hindrances. The results of the EFA emphasize the significant impact of risk and privacy barriers in impeding the adoption of CC. The present study offers valuable insights that may inform policymakers in their efforts to enhance the effectiveness of construction projects by addressing the issue of data fragmentation. Through the identification and addressing of these limitations, the research study makes a valuable contribution to the optimization of data fragmentation procedures. Furthermore, it promotes the sustainability of existing building projects by facilitating the more effective exploitation of cloud computing. This paper presents a novel methodology that establishes a foundation for identifying and tackling the many barriers that impede the broad adoption of cloud computing in the construction sector.

In the construction industry, managing health and safety concerns is paramount to preventing accidents, injuries, and project delays. The integration of Artificial Intelligence (AI) into existing health and safety management systems holds the potential for significantly improving risk detection, mitigation, and overall management [1,2,3]. However, despite the evident benefits of AI in this domain, there remains a notable gap in the literature concerning the essential factors for successful implementation [4,5]. This study aims to address this gap by meticulously analyzing the key elements that contribute to the success of AI integration into the digital transformation of health and safety management systems within cutting-edge construction projects. Our methodology involved the identification of 25 factors, drawn from prior research and industry consensus. These factors were subjected to rigorous analysis, including Exploratory Factor Analysis (EFA) following a pilot survey with field experts and Structural Equation Modeling (SEM) using data obtained from a comprehensive questionnaire distributed among a representative sample of construction industry experts. The study's findings underscore the paramount importance of six critical constructs in determining the success of AI implementation in construction health and safety management systems: Adaptability, Operation, Management, Reliability, Integration, and Knowledge. These findings provide valuable insights for enhancing safety measures in the construction industry through AI-driven solutions.

Construction industry professionals admire the evolution of digital data-acquisition technologies in monitoring processes due to efficient and efficacious outcomes. However, due to a lack of theoretical insight towards the effective application of these technologies, hesitation has been observed for its adoption, which added a challenge to attaining the Industry 4.0 spirit. This study aims to improve a theoretical, statistically validated model, underlining the operational factors that enhance the performance of the digitized monitoring process. The study has followed the structural equation modeling (SEM) approach on identified 36 factors, which were colligated under four categories for developing the technological-based model. The attained model defines effective technological-based factors for implementing automated monitoring under ‘tracking & sensing’, ‘site video’, ‘3D scanner’, and ‘site images’. The significance of this model is a provision of a theoretical base to researchers and construction industry professionals in digitized progress monitoring technological operations and technical aspects towards enriched outcomes.

Monitoring the construction productivity (CP) is significant for the success of construction projects as it helps to assess the project performance. This study aims to investigate the relation between automated monitoring (AM) technologies and CP enhancement. To achieve this, the different AM technologies that are used for monitoring in building projects, factors affecting CP, and factors of CP that can be monitored using AM were investigated and prioritized. A mixed methodology including a systematic review and analytic hierarchy process was adopted. Results provide that; CP factors can be detected and enhanced effectively by monitoring CP. Among the adopted AM technologies, vision-based (37%) is the most effective one in monitoring CP. While workforce factors are the most important factors affecting CP. Also, in terms of factors monitored using AM technologies, factors within the workforce category come in the first place. Consonantly, it is rational to focus on monitoring these factors to enhance CP.

As new technological advances are incorporated within the construction sector, the construction industry is exposed to numerous new challenges regarding construction safety. This study probes a Systematic Literature Review (SLR) method for the Personal Protective Equipment (PPE) adopted within the construction projects. The study has been subgenre into three phases, i.e. Data Acquisition, Data Analysis, and Data Interpretation. Overall, 167 records were identified by filtering through Scopus, Science Direct, Web of Science, and Google Scholar. Based on the filtered literature, a clustered network visualization diagram was developed in VOS viewer. Smart PLS was further used for a detailed analysis of acquisition data. The study produced a conceptual model evaluation based on Smart PLS and recommends focusing on its role in safety, safety instruction and exercise, workers monitoring mechanism, and workers safety problems. The maximum R2 value for the construct safety instruction and exercise was 0.815 generated by this analysis results.

Competitive advantage significantly matters for modern construction organizations as it promotes the sustainable development and safety management of projects. UK construction organizations have greatly suffered from a lack of safety and sustainability of project management in infrastructure development projects because of the COVID-19 pandemic. Post-COVID-19 UK infrastructure development projects are uncertain in the context of organizational competitive advantage as they cannot maintain sustainable project management. This study was conducted to identify, rank, and present a framework of factors influencing the competitive advantage of UK organizations. The study design involved identifying factors from the current literature, after which the most relevant factors were filtered with the help of semi-structured interviews with 15 experts. A pilot survey was conducted then, involving 192 respondents, after which a reliability test and exploratory factor analysis (EFA) were conducted on the results. A primary questionnaire survey involving 250 respondents was conducted, after which RII and confirmatory factor analysis (CFA) were conducted on the final data. A total of five subgroups were found to be significant in the CFA, such as Resources and Policies, Quality and Delays, Motivation and Expectation, Management and Environment, and Government and Business. It is recommended based on findings that the construction organizations of the UK have to consider working on identified factors to maintain a competitive advantage in establishing sustainable project management solutions for post-COVID-19 UK infrastructure development projects.

Automated monitoring technologies are being praised in the construction sector due to effective and efficient outcomes. However, hesitation remains towards its adoption, and one of the reasons is the lack of theoretical understanding towards effective implementation. This study aims to develop a theoretical framework highlighting the factors impacting the effective performance of automated monitoring. Overall, 36 factors were identified under four technological categories. The knowledge framework has been developed with the help of data collected by adopting a structured literature review, questionnaire-based survey, and statistical analysis techniques. The developed technological framework highlights the most impactful factors for each close-range automated monitoring process and technology. This study provides the base conceptual foundation to stakeholders towards the theoretical understanding of automated monitoring processes and technologies for factors that impact the outcomes.

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.

As the global population continues to grow, efficient construction methods without compromising project quality are needed. This study identified the critical success factors (CSFs) required for adoption of Industrialized Building System (IBS) in Malaysian construction industry (MCI). Preliminary factors influencing adoption of IBS from previous studies were contextually adjusted using pilot study. These factors were categorized into five areas: stakeholder understanding, resource availability, process management, issues and perceptions, and future needs. Questionnaires were then administered to 314 IBS players within MCI followed by data analysis methods. Relative Importance Index (RII) ranked increasing skilled labor, introducing renovation capabilities, and removing negative social perception as the main CSFs for adoption of IBS in MCI. The results of this study will be a guide for construction stakeholders to improve efficiency in construction by introducing IBS in MCI.