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Application of emerging digital technology (DT) is an attractive subject of research and is indispensable to the current goal of intelligent construction in many countries. Relevant studies have either focused on the status of a specific DT or proposed preliminary/qualitative comparisons among DTs. This paper provides a systematic understanding of research on applying DTs to the architecture, engineering, construction, and operation (AECO) industry using a mixed-review method. Ten emerging DTs are determined, and publications related to them in 2011–2020 are collected from the Scopus database. The overall status and comparisons of research on 10 emerging DTs by region, journal, and popularity are then presented. The content of research on them is examined through in-depth literature analysis (including research scenario, project stage, and literature evolution in a discipline) and theme classification. Future research directions including technology knowledge, development, application and management, and some practical implications for the country, industry, project, and enterprise, are finally proposed. This research will be informative for academics with regard to the emerging DT's opportunities in this discipline and provide support for work on the digitization of the AECO industry. Some practical implications are proposed from the perspectives of the country, industry, project, and enterprise. For the government, the holistic planning and scientific coordination of emerging DTs need to be highlighted, such as vigorously promoting Building Information Modelling (BIM) and artificial intelligence (AI) and utilizing them to further support the progress of blockchain and digital twin. Establishing demonstration projects to play their radiating roles and accelerate the inter-regional promotion of emerging DTs is also effective. For the industry, it is suggested to build technology alliances to jointly develop integrated platforms covering multiple emerging DTs through in-depth synergy; industry associations are advised to strengthen knowledge diffusion and accumulation for emerging DTs through sharing and training. For projects, applying emerging DTs to the entire life of projects and making them be deeply integrated with off-site, lean, and green construction are advisable. For enterprises, improving the workflow and management model adapted to the application of emerging DTs is necessary. Through adjusting organizational structure and transforming corporate culture, the competitiveness of enterprises could be continuously improved. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) can integrate multi-stage resources and promote multi-agent collaboration. As a key information technology in the architecture, engineering and construction field, BIM has attracted increasing attention worldwide. Existing research mainly focuses on influencing factors or project-based applications of BIM, lacking a macroscopic and quantitative measurement. This study quantitatively explores the macro characteristics and dynamics of BIM adoption. A total of 4,591 BIM-related patents were collected, and the current BIM adoption status was clarified. The evolution process of BIM adoption from 2011 to 2020 was revealed using social network analysis, and the development trend was predicted based on a Barrat, Barthelemy, Vespignani (BBV) model. The results indicate that BIM adoption in China presents significant regional imbalances and scale-free network features, with low connectivity as a whole and a significant island effect locally, and is entering a stage of rapid development and active evolution. This study provides an overview of actual BIM adoption and its potential causes, which is a breakthrough in existing research. The conclusions will contribute to the government issuing evidence-based policies to promote BIM at large. [ABSTRACT FROM AUTHOR]

Off-site construction (OSC) has received increasing attention worldwide, given its advantages over conventional counterparts. However, the performance of OSC's practice has yet to be improved, despite the significant policy effort spent. This paper explores OSC project development strategy optimization from the synergetic perspective of the government and developers. A dynamic Stackelberg game is constructed and, after rigorous theoretical deductions, an example analysis is conducted to determine a proper algorithm for the model's solution. Using Changchun City, China, as an example, the data are collected from policy documents and public web information, and through use of the Delphi method. The optimal development strategies of the local government and developers and the influences of key parameters are then obtained. The synergetic optimization model proposed by this study is conducive to achieving a "win-win" goal of maximizing both developers' economic benefits and the total social benefits. This is a breakthrough in the previous research, contributing to effectively promoting OSC within and beyond the Chinese context. [ABSTRACT FROM AUTHOR]

Prefabricated construction (PC) is an effective approach for addressing the unsustainability issues of conventional construction methods. Despite considerable concern from the China's government, the provincial development level of PC remains unclear. Lacking PC-related statistics hinders relevant research, while data obtained from new media (such as Weibo and WeChat) using web crawler can bridge this gap. This study proposed a new media-driven measurement for the development level of PC, targeting 31 provinces in China. A total of 28 alternative indexes were firstly selected based on the Politics-Economy-Society-Technology (PEST) analysis model, and the final 15 indexes were filtered through expert scoring and new media data-driven methods. Then, web crawling and text segmentation by Python were employed to acquire index data, supplemented by statistics. The indexes weights were determined through expert scoring and a cloud model. The quantitative measurement of the development level of PC was finally performed. The results mainly indicate the holistic development level of PC in China is currently low and its regional distribution is uneven, with high scores in the eastern provinces, and the western and northwestern provinces lagging behind The measurement system construction and the measurement index quantification driven by new media data in this study represent breakthroughs over traditional methods that rely on statistics, cases or questionnaires, which can be applied to other research fields. In addition, the measurement system constructed provides a reference to measure the PC development level in other countries. • A measurement system for development level of prefabricated construction is built. • New media data is used to quantify indexes and improve measurement system. • Index weights are calculated by combining expert scoring and a cloud model. • The holistic measurement method is validated by an empirical analysis in China. • Measurement driven by new media data is applicable to other fields and countries. [ABSTRACT FROM AUTHOR]

The scheduling of prefabricated component production is crucial for production efficiency and timely delivery in off-site construction (OSC). However, the prevalence of mixed-flow production in prefabrication renders conventional scheduling methods inadequate, leading to cost overruns and waiting issues. This study introduces a lean-based mixed-flow production scheduling approach to address the scheduling challenges in mixed-flow prefabrication. Guided by lean principles, this research analyzes the mixed-flow production process and process wastes, resolving issues of group production, pull production, and overall scheduling enhancement through multiobjective optimization modeling. A nondominated sorting genetic algorithm II (NSGA-II) based solution algorithm was established to minimize completion times, tardiness/earliness penalty, and type changes, thereby eliminating waste and ensuring continuous production. The effectiveness of the proposed approach was validated through a real case study, showing an average 1.84% reduction in makespan, 24.19% cost savings, and 36.03% reduction in waiting times compared to conventional nonlean optimization methods, while demonstrating good stability and reliability. This study innovates in engineering scheduling management and contributes to the development of lean construction theory, providing concrete support for component suppliers' successful delivery and promoting further development in OSC. [ABSTRACT FROM AUTHOR]

Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application. [ABSTRACT FROM AUTHOR]

Many countries and regions consider off-site construction a modern construction method that facilitates sustainability in the construction industry. Sustainability research in off-site construction (SROSC) is crucial for its development and is a nonnegligible part of its management. However, an in-depth understanding and critical analysis of SROSC to summarize recent research and inform future research directions are lacking. In this study, we address this issue by offering a mixed-review method integrating scientometric analysis and systematic review to explore state-of-the-art SROSC. We aim to uncover sustainability themes and topics, distinguish research trends, and identify gaps in knowledge that can be addressed in future research. Scientometric analysis was used to perform statistical analysis and visual map research on the knowledge landscape formed by 272 related studies of SROSC. The systematic review was used to identify and analyze six major knowledge themes (e.g., environmental, economic, social, decision-making, optimization, and industry management) and 21 knowledge topics for SROSC. We proposed possible future research directions based on the resulting structured body of knowledge. This research contributes to the body of knowledge by visualizing and analyzing the state-of-the-art of SROSC, as well as identifying the future research directions in this area to improve architectural, engineering, and construction practices. [ABSTRACT FROM AUTHOR]

Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep learning network named dual attention-based point cloud network (DAPCNet) is developed in this paper to act on point clouds for segmentation. In the developed model, data normalization and feature aggregation are first processed to eliminate data discrepancies and enhance local features, after which the processed data are input into the built network layers based on the encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. Furthermore, a custom loss function called Facal Cross-Entropy ("FacalCE") is designed to enhance the model's ability to extract and learn features while addressing imbalanced data distribution. To validate the effectiveness and feasibility of the developed model, a dataset of tunnel point clouds collected from a real engineering project in China is employed. The experimental results indicate that (1) the developed model has excellent performance with Mean Intersection over Union (MIoU) of 0.8597, (2) the improved 3D dual attention module and "FacalCE" contribute to the model performance, respectively, and (3) the developed model is superior to other state-of-the-art methods, such as PointNet and DGCNN. In summary, the DAPCNet model exhibits exceptional performance, offering effective and accurate results for segmenting multi-class objects within tunnel point clouds. • A deep learning method named DAPCNet is developed for 3D point cloud segmentation. • An improved 3D dual attention module is introduced to enhance model performance. • A custom loss "FacalCE" is designed to strengthen feature learning with handling data imbalance. • Conducting comparisons to examine the model performance for segmentation. • The developed method demonstrates outstanding performance, achieving an MIoU score of 0.8597 when applied to tunnel point clouds. [ABSTRACT FROM AUTHOR]