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This study aims to develop a novel theoretical model for predicting the users’ intention to use virtual tools designed for construction risk evaluation. Risk evaluation is a vital objective for construction managers. This paper intends to examine critical factors such as potential benefits, motivation, performance expectancy and rich sources of information that may affect users’ intention to use virtual technology.

A pile training module (PTM) was developed in a virtual environment to analyze the proposed virtual reality-technology acceptance model (VR-TAM) factors. Further, a questionnaire survey was conducted with the participation of 102 construction professionals in China to validate the proposed VR-TAM model and PTM tool. The retrieved data was computed to test the proposed model by using partial least squares structural equation modeling and the significance of the PTM tool in a virtual environment.

The results of this study reveal that high-significance paths represent five relationships between crucial factors affecting users’ intention to use a selected virtual reality (VR) module. Five of seven hypothesis paths were significant with acceptable t-values. By quantitative measurement of high-significance paths, this research has found that each factor under VR-TAM has received significant loadings, with many above the 0.7 threshold mark and others around 0.6. The top factors include “motivation” and “benefits” and have multiplier effects on “intention to use” as the source factors.

The finding of this study presents crucial factors for VR adoption, and the proposed VR-TAM model contributes to the body of knowledge toward managing construction risk using pre-optimization and understanding in a virtual environment. This study supports Chinese construction company managers in effectively using VR technology in their construction projects for risk assessment and management.

This study offered the development of a novel VR-TAM integrated with risk assessment techniques for piling processes. Further, the developed model was analyzed by using a survey of Chinese construction professionals to collect perceptions about the modified theoretical model of VR-TAM.

Advanced construction technologies (ACTs) are transforming infrastructure projects, yet there has been little research into and theorization of the process by which these innovations are diffused. The purpose of this paper is to address this paucity of research by exploring the problems of information asymmetries between vendors and customers in the ACT diffusion process. Specifically, the paper explores whether information asymmetries exist between vendors and customers in the ACT diffusion process and what forms they take.

A structured survey of 153 vendors and customers of advanced construction technologies was undertaken across three international ACT exhibitions in Australia.

By comparing the perspectives of both customers and vendors across 15 technology diffusion process variables using importance-performance analysis and principal component analysis, significant differences are found between vendors’ and customers’ perceptions of how effectively information flows in the ACT diffusion process. The results show that vendors are significantly more optimistic than customers about information asymmetries on a wide range of diffusion variables. They also highlight significant potential for information asymmetries to occur which can undermine the advanced technology diffusion process.

The results provide important new conceptual and practical insights into an under-researched area, which is of increasing importance to a major industry, which is being transformed by advanced technological developments.

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

This paper aims to present the sustainable performance criteria for 3D printing practices, while reporting the primarily computations and lab experimentations. The potential advantages for integrating three-dimensional (3D) printing into house construction are significant in Construction Industry 4.0; these include the capacity for mass customisation of designs and parameters for functional and aesthetic purposes, reduction in construction waste from highly precise material placement and the use of recycled waste products in layer deposition materials. With the ultimate goal of improving construction efficiency and decreasing building costs, applying Strand7 Finite Element Analysis software, a numerical model was designed specifically for 3D printing in a cement mix incorporated with recycled waste product high-density polyethylene (HDPE) and found that construction of an arched truss-like roof was structurally feasible without the need for steel reinforcements.

The research method consists of three key steps: design a prototype of possible structural layouts for the 3DSBP, create 24 laboratory samples using a brittle material to identify operation challenges and analyse the correlation between time and scale size and synthesising the numerical analysis and laboratory observations to develop the evaluation criteria for 3DSBP products. The selected house consists of layouts that resemble existing house such as living room, bed rooms and garages.

Some criteria for sustainable construction using 3DP were developed. The Strand7 model results suggested that under the different load combinations as stated in AS1700, the maximum tensile stress experienced is 1.70 MPa and maximum compressive stress experienced is 3.06 MPa. The cement mix of the house is incorporated with rHDPE, which result in a tensile strength of 3 MPa and compressive strength of 26 MPa. That means the house is structurally feasible without the help of any reinforcements. Investigations had also been performed on comparing a flat and arch and found the maximum tensile stress within a flat roof would cause the concrete to fail. Whereas an arch roof had reduced the maximum tensile stress to an acceptable range for concrete to withstand loadings. Currently, there are a few 3D printing techniques that can be adopted for this purpose, and more advanced technology in the future could eliminate the current limitation on 3D printing and bring forth this idea as a common practice in house construction.

This study provides some novel criteria for evaluating a 3D printing performance and discusses challenges of 3D utilisation from design and managerial perspectives. The criteria are relied on maximum utility and minimum impact pillars which can be used by scholars and practitioners to measure their performance. The criteria and the results of the computation and experimentation can be considered as critical benchmarks for future practices.

This paper aims to identify the relevant contributing constructs of readiness for the implementation of intelligent contracts (iContracts) in the construction industry. This study investigates the relationship between the personality dimensions of technology readiness index (TRI) and the system specific factors of technology acceptance model (TAM) within the context of iContracts.

Drawing insights from the extant literature and the author's previous qualitative investigations into iContract readiness constructs, a quantitative approach is used to operationalise the constructs by offering relevant statements to be measured and validated through a multiple-item scale against the users intent to accept the future iContract technology.

This study confirms and validates the relationship of the proposed iContract readiness index (iCRI) statements against the established TAM factors by offering 18 new constructs influencing technology readiness of the iContract technology. This study proves 9 of the 12 hypotheses highlighting key factors to be addressed for the successful development of the iContract technology.

This paper contributes to the body of knowledge by proposing a novel iCRI that informs an iContract technology readiness acceptance model (iCTRAM) for a trending technology. The iCTRAM can guide developers in producing an appropriate iContract solution and assess the readiness of users and organisations for the successful adoption of the iContract concept.

This study offers a unique theoretical framework, in an embryonic field, for predicting the success of iContract implementation within construction organisations. This study combines the established studies of TRI and TAM in producing a predictive iContract readiness assessment tool.

This paper aims to examine the current technology acceptance model (TAM) in the field of mixed reality and digital twin (MRDT) and identify key factors affecting users' intentions to use MRDT. The factors are used as a set of key metrics for proposing a predictive model for virtual, augmented and mixed reality (MR) acceptance by users. This model is called the extended TAM for MRDT adoption in the architecture, engineering, construction and operations (AECO) industry.

An interpretivist philosophical lens was adopted to conduct an inductive systematic and bibliographical analysis of secondary data contained within published journal articles that focused upon MRDT acceptance modelling. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to meta-analysis were adopted to ensure all key investigations were included in the final database set. Quantity indicators such as path coefficients, factor ranking, Cronbach’s alpha (a) and chi-square (b) test, coupled with content analysis, were used for examining the database constructed. The database included journal papers from 2010 to 2020.

The extant literature revealed that the most commonly used constructs of the MRDT–TAM included: subjective norm; social influence; perceived ease of use (PEOU); perceived security; perceived enjoyment; satisfaction; perceived usefulness (PU); attitude; and behavioural intention (BI). Using these identified constructs, the general extended TAM for MRDT in the AECO industry is developed. Other important factors such as “perceived immersion” could be added to the obtained model.

The decision to utilise a new technology is difficult and high risk in the construction project context, due to the complexity of MRDT technologies and dynamic construction environment. The outcome of the decision may affect employee performance, project productivity and on-site safety. The extended acceptance model offers a set of factors that assist managers or practitioners in making effective decisions for utilising any type of MRDT technology.

Several constraints are apparent due to the limited investigation of MRDT evaluation matrices and empirical studies. For example, the research only covers technologies which have been reported in the literature, relating to virtual reality (VR), augmented reality (AR), MR, DT and sensors, so newer technologies may not be included. Moreover, the review process could span a longer time period and thus embrace a fuller spectrum of technology development in these different areas.

The research provides a theoretical model for measuring and evaluating MRDT acceptance at the individual level in the AECO context and signposts future research related to MRDT adoption in the AECO industry, as well as providing managerial guidance for progressive AECO professionals who seek to expand their use of MRDT in the Fourth Industrial Revolution (4IR). A set of key factors affecting MRDT acceptance is identified which will help innovators to improve their technology to achieve a wider acceptance.

The application of integrated project delivery (IPD) in conjunction with building information modeling (BIM) and Lean Construction (LC) as the efficient method for improving collaboration and delivering construction projects has been acknowledged by construction academics and professionals. Once organizations have fully embraced BIM, IPD and LC integration, a measurement tool such as a maturity model (MM) for benchmarking their progress and setting realistic goals for continuous improvement will be required. In the context of MMs literature, however, no comprehensive analysis of these three construction management methods has been published to reveal the current trends and common themes in which the models have approached each other.

Therefore, this study integrates systematic literature review (SLR) and thematic analysis techniques to review and categorize the related MMs; the key themes in which the interrelationship between BIM, IPD and LC MMs has been discussed and conceptualized in the attributes; the shared characteristics of the existing BIM, IPD and LC MMs, as well as their strengths and limitations. The Preferred Reporting Items for Systematic Reviews (PRISMA) method has been used as the primary procedure for article screening and reviewing published papers between 2007 and 2022.

Despite the growth of BIM, IPD and LC integration publications and acknowledgment in the literature, no MM has been established that holistically measures BIM, IPD and LC integration in an organization. This study identifies five interrelated and overlapping themes indicative of the collaboration of BIM, IPD and LC in existing MMs' structure, including customer satisfaction, waste minimization, Lean practices and cultural and legal aspects. Furthermore, the MMs' common characteristics, strengths and limitations are evaluated to provide a foundation for developing future BIM, IPD and LC-related MMs.

This paper examines the current status of research and the knowledge gaps around BIM, IPD and LC MMs. In addition, the highlighted major themes serve as a foundation for academics who intend to develop integrated BIM, IPD, and LC MMs. This will enable researchers to build upon these themes and establish a comprehensive list of maturity attributes fulfilling the BIM, IPD and LC requirements and principles. In addition, the MMs' BIM, IPD and LC compatibility themes, which go beyond themes' intended characteristics in silos, increase industry practitioners' awareness of the underlying factors of BIM, IPD and LC integration.

This review article is the first of a kind to analyze the interaction of IPD, BIM and LC in the context of MMs in current AEC literature. This study concludes that BIM, IPD and LC share several joint cornerstones according to the existing MMs.