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In the current era of technological advancement, the architectural, engineering and construction (AEC) industry is undergoing a radical transformation, prompting researchers to explore new breakthroughs that can revolutionise the construction process. This paper delves into the use of cutting-edge technologies such as building information management (BIM), blockchain and the Internet of Things (IoT), along with advanced management techniques such as work breakdown structure (WBS) and Agile thinking, to enhance the industry’s efficiency, productivity, quality and cost-effectiveness. Moreover, the pressing need for a sustainable, secure and transparent sector amplifies the significance of the proposed research.

This study’s research approach comprises an intensive literature review to construct a conceptual framework, followed by an exploratory questionnaire to validate the framework.

This paper demonstrates how blockchain combined with a WBS and a BIM platform may boost collaboration in order to experience efficient and trusted workflow scenarios that can overcome many of the challenges given in a traditional building technique. The research findings emphasise the benefits of the proposed new mentality approach, which incorporates all of the previously described tools/techniques to the business.

This paper highlights the advantages of leveraging a combination of blockchain, WBS and BIM platforms to boost collaboration and enable efficient and trustworthy workflow scenarios that can surmount the difficulties inherent in traditional AEC industry collaboration methods.

This study provides original insights into the challenges and opportunities of using blockchain for AEC collaboration, by exploring the potential of decentralised blockchain networks to improve the security, efficiency and transparency of collaborative data sharing and management.

Extended reality (XR) is an emerging technology, with its popularity rising in different industry sectors, where its application has been recently considered in construction safety. This study aims to investigate the applications of XR technologies in the safety of construction through projects lifecycle perspective.

Scientometric analysis was conducted to discover trends, keywords, contribution of countries and publication outlets in the literature. The content analysis was applied to categorize previous studies into three groups concerning the phase of lifecycle in which they used XR.

Results of the content analysis showed that the application of XR in the construction safety is mostly covered in two areas, namely, safety training and risk management. It was found that virtual reality was the most used XR tool with most of its application dedicated to safety training in the design phase. The amount of research on the application of augmented reality and mixed reality in safety training, and risk management in all phases of lifecycle is still insignificant. Finally, this study proposed three main areas for using the XR technologies regarding the safety issues in future research, namely, control of safety regulations and safety coordination in construction phase, and safety reports in the operation phase.

This paper inspected the utilization of all types of XR for safety in each phase of construction lifecycle and proposed future directions for research by addressing the safety challenges in each phase.

Policymakers are developing government-level pandemic response strategies (GPRS) to assist architecture, engineering and construction (AEC) enterprises. However, the effectiveness of the GPRS has not been assessed. Therefore, this study aims to investigate the interrelationships between GPRS and AEC enterprises. To achieve that aim, the study objectives are to compare GPRS effectiveness between small-medium and large AEC enterprises, develop groupings to categorize interrelated GPRS and evaluate the effectiveness of the GPRS and interrelated constructs.

A systematic literature review and semi-structured interviews with 40 AEC industry professionals were carried out, generating 22 GPRS. Then, questionnaire survey data was collected among AEC professionals. In total, 114 valid survey answers were received and analyzed using the Kruskal–Wallis H test, normalized mean analysis, factor analysis and fuzzy synthetic evaluation.

Small-medium enterprises have four distinct critical GPRS: “form a special task force to provide support in maneuvering COVID-19,” “provide infrastructure investment budgets to local governments,” “develop employee assistance programs that fit all types of working groups” and “diversify existing supply chain.” Large enterprises have two distinct critical GPRS: “provide help in digitalizing existing construction projects” and “mandate COVID-19 as force majeure.” Eighteen GPRS can be categorized into the following five constructs: “market stability and financial aid,” “enterprise capability management,” “supply chain improvement,” “law and policy resources” and “information and workforce management.” The former two constructs are more effective than other GPRS constructs.

This is the first paper that evaluates the effectiveness of GPRS for AEC enterprises, providing new evidence to policymakers for well-informed decision-making in developing pandemic response strategies.

The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners.

Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses.

The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT.

These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field.

This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.

In recent years, augmented reality (AR) has shown its potential to assist various construction activities. Its use commonly requires additional refinement to be integrated into the building information modeling (BIM) process. Nevertheless, few studies have investigated AR implementation in BIM-enabled projects because of numerous challenges related to its implementation. This study aims to investigate the implementation of AR in construction and identify the critical mechanisms for implementing BIM-AR successfully.

A mixed methodology was adopted for this study. First, this work presents a bibliometric analysis covering articles obtained from Scopus database published between 2000 and 2022. A sample size of 65 research papers pertinent to AR in construction was analyzed using VOSviewer software. Second, a participatory case study was conducted for a BIM-enabled project in China to gain insight into how BIM-AR implementation in construction is achieved.

The findings from the bibliometric analysis show an increasing interest in AR research within construction. The results indicate that AR research focuses on four clusters: real-time communication, project management, construction activities and education. Findings from the case study provide an empirical experience of AR application scenarios in a BIM-enabled project. Concomitantly, 15 critical success factors that influence BIM-AR implementation were finally identified and demonstrated.

This study provides a rich insight into the understanding and awareness of implementing AR. First, the findings are beneficial to construction practitioners and researchers because they provide a concentrated perspective of AR for emerging activities in the construction industry. Second, the results obtained from the case study could provide a useful guide for effectively implementing AR in a BIM-enabled construction project. Overall, this study may stimulate further research on AR-related studies in construction, such as BIM integration, factor analysis and construction education.

This study presents an ontological approach to assess the architectural outputs of generative adversarial networks. This paper aims to assess the performance of the generative adversarial network in representing building knowledge.

The proposed ontological assessment consists of five steps. These are, respectively, creating an architectural data set, developing ontology for the architectural data set, training the You Only Look Once object detection with labels within the proposed ontology, training the StyleGAN algorithm with the images in the data set and finally, detecting the ontological labels and calculating the ontological relations of StyleGAN-generated pixel-based architectural images. The authors propose and calculate ontological identity and ontological inclusion metrics to assess the StyleGAN-generated ontological labels. This study uses 300 bay window images as an architectural data set for the ontological assessment experiments.

The ontological assessment provides semantic-based queries on StyleGAN-generated architectural images by checking the validity of the building knowledge representation. Moreover, this ontological validity reveals the building element label-specific failure and success rates simultaneously.

This study contributes to the assessment process of the generative adversarial networks through ontological validity checks rather than only conducting pixel-based similarity checks; semantic-based queries can introduce the GAN-generated, pixel-based building elements into the architecture, engineering and construction industry.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Those who believe they excel at architecture or engineering education are more likely to succeed based on self-efficacy principles. To investigate educator self-efficacy and success in the Online Learning Environment (OLE), a set of relationships are observed which describe correlations between experience and potential.

An online survey instrument was distributed the fall after COVID-19 university closures. Respondents were asked to reflect on their level of experience teaching and their ability to teach online. All analyzed data were subjected to descriptive and inferential statistics using the SPSS 22.0 statistical software package. The compatibility of the variables with normal distribution was tested using the Kolmogorov–Smirnov and Shapiro–Wilk methods. Variables comparisons were performed using non-parametric tests (Kruskal Wallis test, Mann Whitney U test). The relationships between quantitative variables were examined using the Spearman Rank Correlation and comparisons formed from the qualitative variables were tested using the Pearson Chi-Square and Fisher Exact methods.

Educator self-efficacy was determined throughout the COVID-19 transition. Possessing online teaching experience is related to the perceptions that architectural education can be delivered entirely online. A relationship was found for educators who previously taught using OLE and who had experience with delivering and developing OLE.

It is incumbent on educators and administrators to continue to learn how to best accommodate student learning. The strong relationship for R1: Total teaching experience (IV) and perceptions of whether AEC education can be delivered completely online, points to having educators with a depth of experience and being open to change. The strong relationship shown for R2: Have you ever taught using an online method before January 2020 and Experience in developing online materials demonstrates that a variety of experience will also support educators in a time of change. These relationships illustrate how educator efficacy can provide support for educators during times of crisis.

U.S. Architectural and Architectural Engineering educator pandemic OLE self-efficacy has not been previously been a focus of research efforts. This research adds to the body of knowledge by demonstrating how relationships between teaching experience and OLE can encourage educator self-efficacy during a crisis. Statistical analyses found a strong relationship between total teaching experience and perceptions that AEC education can be delivered completely online. A strong relationship was found between online teaching experience and positive experiences in developing online materials.

Digital technology, which is regarded as a prominent and transformational force in modern society, encompasses a wide variety of technology that utilize digital data to process, store and transfer various types of information. Digital technologies have continually been introduced as cutting-edge information tools in order to achieve effective management of vast information that arises from the prefabrication supply chain. However, without a sufficient performance evaluation, drawbacks of technology investment, such as financial losses and ineffective resource allocation, keep occurring, which hinders the widespread implementation of digital technologies. This study demonstrates a comprehensive evaluation of digital technologies’ effects on the prefabrication supply chain based on multi-criteria decision analysis (MCDA) theory.

Specifically, the targeted digital technologies and project constraints were first identified through a systematic literature review. The effects of the digital technologies were then scored using a questionnaire survey. The TOPSIS model was established to quantitatively rank the effectiveness of selected digital technologies.

Overall, BIM technology shone out in the rankings and is regarded as the most beneficial digital solution by multi-stakeholders to the existing constraints, such as working efficiency. Collaboration patterns between different stakeholders and technology integration trend were also indicated.

Compared with existing outcomes, this study specifically focused on examining the effects of digital technologies on the prefabrication supply chain, the most significant link in the process for prefabricated structures. New findings indicate the overall performance that considered both multi-stakeholders’ preferences and project constraints. The quantitative evaluation presents a comprehensive understanding of digital technologies’ effects, enabling industrial participants to reach well-informed, strategic and profitable investment decisions.

Building information modelling (BIM) implementation in the design, construction and operations (DCO) industry is increasingly becoming essential. While BIM has been adopted on a larger scale in many developed economies, its acceptance is still in the embryonic phases for developing nations in the DCO industry. This study aims to identify the inhibitors to BIM implementation through the social network theoretical lens, intending to understand the associations among the barriers in the Indian context. Subsequently, recommend strategies to mitigate the barriers from the academic practitioner’s perspective.

A mixed methods research was adopted, commencing with comprehensive literature reviews to recognise various inhibitors to BIM implementation. These identified barriers were further examined through the questionnaire survey (n = 71). BIM implementation barrier network (BIBN) was created using University of California at Irvine Network (UCINET) is a powerful social network analysis software that functions on the principle of social network theory. The experts’ opinions were captured through the BIBN network through interviews. Network properties such as eigen vector centrality, betweenness centrality, degree centrality, in-degree and out-degree and clustering coefficient were computed, and the metrics were analysed further.

Twenty-six BIM implementation barriers were initially identified. A questionnaire survey was conducted. The chain reaction can be minimised by prioritising and regulating these barriers. The issues were categorised into fourfold clusters (standardisation, policy and process, cultural and human resources, change management and operational) issues were generated from the exploratory factor analysis (EFA). The obstacles and barriers resulting from the other main barriers associated with it can be minimised by reducing the challenges with high eigenvector centrality but low betweenness importance.

This study proves to accelerate sustainable BIM implementation growth in developing nations; this research study assists BIM stakeholders in developing coping mechanisms to monitor and remove BIM implementation barriers.

Analysing the associativity of the BIM implementation barriers through sociograms for developing nations is a novel concept with this research.