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Vietnam's construction technology (CT) adoption is low when compared to other countries with similar gross domestic product (GDP) per capita resulting in lesser productivity. The research objectives are: (1) To undertake an extensive literature review on CT adoption challenges; (2) To investigate CT adoption challenges unique to Vietnam's construction sector; and (3) To propose data-driven solutions for a greater rate of CT adoption.

A two-stage descriptive survey method was adopted in alignment with the research aim and objectives. Based on the literature review of 215 articles, a questionnaire was designed and administered to experienced construction managers (CM) to identify whether CT has been adopted, barriers to adoption, drivers, and the most popular CT tools. Descriptive statistics were used to summarize the characteristics of interest in the empirical dataset and SPSS-based inferential statistics to estimate the means, frequency counts, variance and test hypotheses that informed the drawing of conclusions concerning the research objectives.

The popular CT tools identified were Autodesk, Microsoft Office and Primavera. The most influential CT adoption barriers: (1) Unknow`n impact on productivity, (2) Late implementation of software in construction projects, (3) Lack of understanding of importance and needs in the construction industry (4) Lack of funds during budget planning for technological advances and implementation (5) Lack of experts required for technological change, and insufficient skills in the industry.

It is expected that the findings could inform data-driven regulatory and practice reforms targeted at increasing greater uptake of CT in Vietnam with potential for replication in countries facing similar adoption challenges.

The findings are intended to support data-driven regulatory and practice improvements aimed at improving CT adoption in Vietnam, with the possibility for replication in other countries facing comparable problems.

Construction projects in Malaysia are often delayed and over budget due to heavy reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) studies, which are less accurate than machine learning (ML) tools. Construction management applications have increasingly used ML tools in recent years and have greatly impacted forecasting. The research aims to identify the most influential LC factors using statistical approaches, collect data and forecast LC models for improved forecasts of LC.

A thorough literature review was completed to identify LC factors. Experienced project managers were administered to rank the factors based on importance and relevance. Then, data were collected for the six highest ranked factors, and five ML models were created. Finally, five categorical indices were used to analyze and measure the effectiveness of models in determining the performance category.

Worker age, construction skills, worker origin, worker training/education, type of work and worker experience were identified as the most influencing factors on LC. SVM provided the best in comparison to other models.

The findings support data-driven regulatory and practice improvements aimed at improving labor issues in Malaysia, with the possibility for replication in other countries facing comparable problems.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

Though widely recognized as essential for improving work performance across various domains, self-efficacy’s specific role in managing construction workforces remains understudied. This knowledge gap restricts our ability to uncover new factors that enhance workforce management effectiveness and ultimately boost construction labor productivity (CLP). To address this, our study proposes and tests a novel model. This model explores the impact mechanism of self-efficacy on CLP by investigating the mediating role of work motivation. By delving into this crucial yet underexplored area, we aim to provide valuable insights for construction project managers and researchers alike, paving the way for more effective workforce management strategies and consequently, improved CLP.

This study utilizes a mixed-method approach, incorporating both qualitative and quantitative methodologies. Data from 112 rebar workers at five construction sites in Vietnam underwent analysis using Cronbach’s alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) to examine the novel research model.

The results indicate a positive and significant association between self-efficacy and CLP. Additionally, work motivation emerged as a full mediator in the relationship between self-efficacy and CLP. Specifically, individuals with higher self-efficacy set ambitious goals and invest more effort in their pursuit, leading to increased work motivation and, ultimately, heightened productivity levels.

The significant implications of the current study extend to construction managers and policymakers alike. Construction managers can leverage the findings to devise targeted interventions aimed at enhancing the self-efficacy and work motivation of their workforce, potentially resulting in noteworthy enhancements in CLP. Policymakers, too, can benefit from these findings by formulating policies that actively support the cultivation of self-efficacy and work motivation among construction workers. Such policies have the potential to foster a more productive and efficient construction industry, aligning with the broader goals of workforce development and industry enhancement.

This study expands existing knowledge by identifying the important role of self-efficacy in work performance enhancement and the mediating role of work motivation in terms of these relationships.

This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to identify these crucial PFs and secondly, to develop a robust performance model capable of effectively measuring and assessing the intricate interdependencies and correlations within ISSPs. By achieving these objectives, the study aimed to provide valuable insights into and tools for enhancing the efficiency and effectiveness of sanitation projects in the construction industry.

To achieve the study's aim, the methodology for identifying the PFs for ISSPs involved several steps: extensive literature review, interviews with Egyptian industry experts, a questionnaire survey targeting industry practitioners and an analysis using the Relative Importance Index (RII), Pareto principle and analytic network process (ANP). The RII ranked factor importance,  and Pareto identified the top 20% for ANP, which determined connections and interdependencies among these factors.

The literature review identified 36 PFs, and an additional 13 were uncovered during interviews. The highest-ranked PF is PF5, while PF19 is the lowest-ranked. Pareto principle selected 11 PFs, representing the top 20% of factors. The ANP model produced an application for measuring ISSP effectiveness, validated through two case studies. Application results were 92.25% and 91.48%, compared to actual results of 95.77% and 97.37%, indicating its effectiveness and accuracy, respectively.

This study addresses a significant knowledge gap by identifying the critical PFs that influence ISSPs within the construction industry. Subsequently, it constructs a novel performance model, resulting in the development of a practical computer application aimed at measuring and evaluating the performance of these projects.