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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.

The purpose of this paper is to address the gap in knowledge by exploring the role of customers and vendors in diffusion of modern equipment technologies into the construction industry.

To address the need to consider both vendors and customers in the innovation diffusion process and the need for in-depth cross-sectional studies, semi-structured interviews were undertaken with 147 participants including 85 vendors and 62 customers of modern construction technologies at company, project and operational levels in Australia and North America. Thematic analysis and an analytic hierarchy process illustrate the critical role of both customers and vendors in the diffusion process of modern equipment technologies.

A new conceptual model is presented which classifies modern equipment technology customers into four categories: visionaries (group I); innovators (group II); pragmatists (group III); and conservatives (group IV) based on the way in which they interact with vendors in the innovation diffusion process. The results also reveal that there is a significant emotional/affective aspect of innovation diffusion decisions which has not been recognised in previous research.

The major contribution of this study is that it analyses the role of both vendors and customers in the equipment technology diffusion process at three different levels (strategic, project and operational) in large corporations and small-to-medium-sized businesses. The findings not only advance construction innovation research beyond traditional linear models of innovation, but also provide new knowledge which enable customers and vendors to interact more effectively in the diffusion of new construction equipment technologies.

Research into the construction industry’s adoption of modern equipment technologies, such as remote-controlled trucks, excavators and drones, has been neglected in comparison to the significant body of research into the adoption of information technology in construction. Construction research has also neglected to adequately consider the important role of vendors in the innovation diffusion process, focussing mostly on the role of the customer. Set within the context of Australia’s construction industry, the purpose of this paper is to address these gaps in knowledge by exploring the role of customers and vendors in the diffusion of modern equipment technologies into the construction industry.

Using contemporary models of innovation diffusion which move beyond the simple dualistic problem of whether innovation is supply-pushed or demand-pulled, 19 semi-structured interviews were undertaken with customers and vendors involved in two major modern equipment technology trade exhibitions in Australia. This was followed by the collection of documentary data in the form of photos, directory books, marketing material, catalogues, websites and booth and exhibition layouts to validate the proposed model and provide insights into vendor marketing strategies. These data were analysed using both content analysis and principal component analysis (PCA).

According to the PCA and content analysis, vendor’s engagement in the adoption of modern equipment technologies falls into three stages that correspond to three stages in the customer’s adoption process. In the first stage, customers identify possible solutions and recognise new technologies following a previous recognition of a need. Vendors provide facilities for attracting potential customers and letting customers know that their technology exists and can help solve the customer’s problem. The second stage involves customers gaining knowledge about the details of the new technology, and vendors focusing on detailed knowledge transfer through written materials and demonstrations of the functionality of the new technology. In the third stage, customers have specific questions that they want answered to assist them in comparing different vendors and solutions. By this stage, vendors have built a close relationship with the customer and in contrast to earlier stages engage in two-way communication to help the customer’s decision process by addressing specific technical and support-related questions.

The originality and value of this research is in addressing the lack of research in modern equipment technology adoption for building construction and the lack of data on the role of vendors in the process by developing a new empirical framework which describes the stages in the process and the ways that customers and vendors interact at each stage. The results indicate that conceptually, as the construction industry becomes more industrialised, current models of innovation adoption will need to develop to reflect this growing technological complexity and recognise that vendors and customers engage differently in the adoption process, according to the type of technology they wish to adopt.

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.

A problematic issue for new approaches to prefabricated timber construction is simply that there is insufficient productivity measurement data to assist estimation of resource usage, speed onsite and best practice. A lack of information potentially results in increased pricing behaviour which may slow the uptake of prefabricated construction. The purpose of this paper is to measure installation productivity onsite for prefabricated timber floor cassette panels and develop sufficient understanding of the process to suggest improved practices.

A time and motion approach, paired with time-lapse photography was used for detailed capture of prefabricated cassette flooring installation processes onsite. An emphasis was placed on work flow around crane cycles from three case study projects. Time and date stamping from 300 crane cycles was used to generate quantitative data and enable statistical analysis.

The authors show that crane cycle speed is correlated to productivity including gross and net crane time scenarios. The latter is refined further to differentiate uncontrolled outlying crane cycles from normally distributed data, representing a controlled work process. The results show that the installation productivity rates are between 69.38 and 123.49 m²/crane-hour, based on normally distributed crane cycle times. These rates were 10.8–26.1 per cent higher than the data set inclusive of outlier cycles. Large cassettes also proved to be more productive to place than small.

The contribution of this research is the focus on cranage as the lead resource and the key unit of measure driving installation productivity (in cassette flooring prefabricated construction), as distinct from past research that focuses on labour and craft-based studies. It provides a different perspective around mechanisation, for resourcing and planning of work flow. Crane cycles provide a relatively easy yet reliably repeatable means for predicting productivity. The time-lapse photographic analysis offers a high degree of detail, accuracy and objectivity not apparent in other productivity studies which serves to enable quantitative benchmarking with other projects.

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.

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.

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.

Emerging Construction Industry 4.0 technologies raise serious questions for construction companies when deciding whether to adopt or reject emerging technologies. Vendors seek to understand what factors are involved in how construction companies make these decisions and how they might vary across different companies. This paper aims to present a systematic, technology adoption decision-making framework for the construction industry which includes the key steps required for the final decision being made by companies up to the commencement of the operation of the technology.

A total of 123 experienced practitioners were interviewed to identify a broad range of tasks relevant to decision-making. Participants known as customers or vendors were chosen to validate the findings of each group by using data triangulation methods. A systematic thematic analysis method was applied in the NVivo environment to analyse the data.

This study identifies the active role of vendors who need to understand how their customers arrive at decisions to increase the rate of technology adoption. This paper also provides insights to new companies and late adopters (reported greater than 50%) about how others arrived at their decisions.

Unlike other technology adoption models, this paper investigates vendors’ corresponding interactions during the decision-making process. This paper also goes beyond previous studies, which focussed on the individual customer’s intention to use a specific technology at a single-stage by developing a multi-stage framework to enable understanding the details of the decision process at the organisational level.

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.