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Automated interior construction progress monitoring (ICPM) has gained increasing academic attention. This emerging research field faces numerous technical challenges that have been noted in previous studies but lack a holistic examination to analyze these challenges and their potential impacts. This study addresses this gap by conducting a systematic review of ICPM technical challenges, collecting related literature from Scopus, Web of Science, and ScienceDirect databases, and utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) framework for filtering and selecting literature. The filtration results in 44 strongly related technical papers. Alongside summarizing these challenges, the study explores their impacts on the entire ICPM automation process and proposes innovative solutions. Specifically, this study highlights the key phases of ICPM automation development, including data acquisition, 3D reconstruction, as-planned modeling, as-built modeling, progress comparison, and progress quantification, and subsequently identifies challenges for each phase, totaling 11 major challenges composed of 41 subchallenges. The data acquisition phase is found to have the most and most severe challenges, and challenges in other phases also impact the automation system performance. This review encompasses the identification of potential issues and proposes corresponding solutions, enabling future researchers to anticipate challenges and develop more advanced and user-friendly monitoring systems. [ABSTRACT FROM AUTHOR]

Labor shortage and low labor productivity are critical issues in the construction industry. Robotics, as a powerful technology to improve productivity in the digitization era, is being used to assist in addressing these problems. Particularly in recent years, interest in construction robotics research has risen remarkably. To gain a deeper understanding of this burgeoning research field, this study provides a bibliometric analysis of 498 related papers retrieved from WoS (Web of Science). Performance analysis and science mapping were used in the paper to identify the research trends, relationship among authors, top publication sources, regional activities, as well as knowledge base and dominant research sub-fields. The results indicated that the number of papers focusing on construction robotics has been continuously growing since 2013. "Automation," "system," and "design" have been the most addressed topics in construction robotics. The findings are indicative of identifying the deficiencies in existing research and provide directions for future research. Those include greater attention paid to the experimental results of on-site construction robots, while ignoring the impacts of practical construction situations, and the development of off-site construction robots needs more support. For theoretical research, there is a lack of studies on human–robot collaboration workflow and management models. Therefore, the study would be valuable in providing practitioners and researchers with a better perspective on the development of construction robotics and facilitating the building of the intellectual wealth of robotics in the construction industry. Our study analyzed 498 papers related to construction robotics from 1974 to 2022 through bibliometric approaches (mathematical and statistical methods). The study counted the numbers of construction robotics papers by year, identified most active scholars and top publication sources, and analyzed the relationship among regions. Most importantly, the paper identified the knowledge base of the research field of construction robotics and reviewed various relevant studies from theoretical and practical application perspectives. [ABSTRACT FROM AUTHOR]

Green buildings have gained increasing momentum during the past decade, evidenced by an uptick in green building construction worldwide. To facilitate green building development, it is crucial to understand the current geographical distribution of green buildings, thereby identifying emerging markets and opportunities for future growth. However, very few studies investigated the spatial characteristics of the green building market and the factors influencing the distribution of green buildings. By examining the national green building market in the United States, we studied the spatial distribution patterns and significant influencing factors of the county-level US green building markets. Cluster mapping and hot spots of the green building markets were identified through spatial autocorrelation analysis. A spatial regression model with high performance (R2 of 0.83) was developed to identify the significant influencing factors. We found a statistically significant clustering phenomenon of county-level US green building markets. The spatial error model reveals that three factors, namely, the number of housing units, gross domestic product, and the local green building company index, significantly influence the number of green buildings at the county level. We also found that the county-level green building market is influenced by these factors of not only the host county, but also the neighboring counties. Our findings provide a useful reference for stakeholders' decision-making process concerning local green building market development. [ABSTRACT FROM AUTHOR]

As a powerful artificial intelligence tool, the Artificial Neural Network (ANN) has been increasingly applied in the field of construction management (CM) during the last few decades. However, few papers have attempted to draw up a systematic commentary to appraise the state-of-the-art research on ANNs in CM except the one published in 2000. In the present study, a scientometric analysis was conducted to comprehensively analyze 112 related articles retrieved from seven selected authoritative journals published between 2000 and 2020. The analysis identified co-authorship networks, collaboration networks of countries/regions, co-occurrence networks of keywords, and timeline visualization of keywords, together with the strongest citation burst, the active research authors, countries/regions, and main research interests, as well as their evolution trends and collaborative relationships in the past 20 years. This paper finds that there is still a lack of systematic research and sufficient attention to the application of ANNs in CM. Furthermore, ANN applications still face many challenges such as data collection, cleaning and storage, the collaboration of different stakeholders, researchers and countries/regions, as well as the systematic design for the needed platforms. The findings are valuable to both the researchers and industry practitioners who are committed to ANNs in CM. [ABSTRACT FROM AUTHOR]

The Australian Bureau of Statistics (ABS) regularly releases statistical information, for the whole of Australia, for public access. Building- and construction-related statistics are important to reflect the status of this pillar industry of Australia and help researchers, practitioners, and investors with decision-making. Due to complex retrieval hierarchy of ABS's website and irregular update frequency, it is usually time-consuming to find relevant information. Moreover, browsing the raw data from ABS's webpages could not provide the insights to the future. In this work, we applied techniques from computer science to help users in the building and construction domain to better explore the ABS statistics and forecast the future trends. Specifically, we built an integrated Web application that could help collect, sort, and visualize the ABS statistics in a user-friendly and customized way. Our Web application is publicly accessible. We further injected our insights into the Web application, based on the existing data by providing online forecasting on user's interested information. To achieve this, we identified a series of related economic factors as features and adjusted a multi-variant, LSTM-based time series forecasting model by considering the most informative factors. We also compared our approach with the most widely used SARIMA-based forecasting model to show the effectiveness of the deep learning-based models. The forecast values are depicted at the end of the time series plots, selected by the users. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future. [ABSTRACT FROM AUTHOR]

This paper aims to critically review the current body of literature relating to the calculation methods of construction material stock. To this end, this study adopts a systematic literature review technique in order to identify the relevant studies. The findings revealed that the bottom-up and top-down methodologies were commonly employed by the reviewed studies. Based on the findings, it is recommended that the bottom-up approach should be utilized when dealing with small-scale areas or where more accurate results are required. The top-down method should be used wherein the research area is large, and the results could be estimated based upon assumptions and statistical data. Similarly, the demand-driven methodology should be used to find the material stock accumulation due to socio-economic factors. The study also found that the material stock results can be used as data for other research, such as waste management and embodied energy. Further, this paper proposes a conceptual framework to ease the process of calculating construction material stocks in different projects. The outcomes of this research shall be beneficial for future studies that explore the literature connected to the construction material stock and recommend methods and techniques that should be used to quantify the material stock. [ABSTRACT FROM AUTHOR]

Prefabricated buildings are the direction of the future development of the construction industry and have received widespread attention. The effective execution of prefabricated construction project scheduling should consider resource constraints and the supply arrangement of prefabricated components. However, the traditional construction resource-constrained project scheduling implementation method cannot simultaneously consider the characteristics of the linkage between component production and on-site assembly construction. It cannot also fully adapt to the scheduling implementation method of the prefabricated construction projects. It is difficult to work out a reasonable project schedule and resource allocation table. In order to determine the relevant schedule parameters that can reflect the actual construction situation of the prefabricated building and meet the scheduling requirements of the prefabricated project, this study proposes a prefabricated construction project scheduling model that considers project resource constraints and prefabricated component supply constraints. Additionally, it improves the design of traditional genetic algorithms (GAs). Research results of the experimental calculation and engineering application show that the proposed project scheduling optimization model and GA are effective and practical, which can help project managers in effectively formulating prefabricated construction project scheduling plans, reasonably allocating resources, reducing completion time, and improving project performance. [ABSTRACT FROM AUTHOR]

Construction activities taken place in ecologically fragile regions (EFRs) of China are facing a series of environmental obstacles. Studying critical success factors (CSFs) to arrive at the sustainable objectives for construction project in EFRs is needed. Understanding the interrelationships of these CSFs is one of the vital ways to achieve this. This paper identifies and analyzes 18 CSFs for construction projects in EFRs through a literature review from a multi-perspective and a case study of Korla City in China. The causal relationship between each CSF is obtained by pairwise comparisons and thereafter, an ISM (Interpretative Structural Modeling) method is employed to study the hierarchical structuring of the CSFs. As a result, we established a five-level ISM. Subsequently, an MICMAC (cross-impact matrix multiplication applied to classification) approach is implemented to partition and classify each CSF into four quadrants (independent, linkage, autonomous, and dependent) according to their driver and dependence powers. Through the implementation of an MICMAC approach, the degrees of relationship between each CSF is gained. The findings reveal that the studied 18 CSFs have a strong hierarchy and interrelationship. The project manager's leadership style and economic viability are the root source of project success and has the highest influence, which is supported by the result of MICMAC analysis. CSF planning and implementation of sustainable strategies are more dependent and are influenced by others. The CSFs on the top level of ISM: conflict resolution, planning and implementation of sustainable strategies and resources of water play a significant role in arriving at the project success, and has a great potential for future study. The approaches implemented in this paper can be helpful for decision-makers and managers of construction projects in comprehending the interrelationships and the degrees of CSFs for construction projects in EFRs and for efficiently achieving the project success. [ABSTRACT FROM AUTHOR]

Quality control is essential to a successful modular construction project and should be enhanced throughout the project from design to construction and installation. The current methods for analyzing the assembly quality of a removable floodwall heavily rely on manual inspection and contact-type measurements, which are time-consuming and costly. This study presents a systematic and practical approach to improve quality control of the prefabricated modular construction projects by integrating building information modeling (BIM) with three-dimensional (3D) laser scanning technology. The study starts with a thorough literature review of current quality control methods in modular construction. Firstly, the critical quality control procedure for the modular construction structure and components should be identified. Secondly, the dimensions of the structure and components in a BIM model is considered as quality tolerance control benchmarking. Thirdly, the point cloud data is captured with 3D laser scanning, which is used to create the as-built model for the constructed structure. Fourthly, data analysis and field validation are carried out by matching the point cloud data with the as-built model and the BIM model. Finally, the study employs the data of a removable floodwall project to validate the level of technical feasibility and accuracy of the presented methods. This method improved the efficiency and accuracy of modular construction quality control. It established a preliminary foundation for using BIM and laser scanning to conduct quality control in removable floodwall installation. The results indicated that the proposed integration of BIM and 3D laser scanning has great potential to improve the quality control of a modular construction project. [ABSTRACT FROM AUTHOR]

In the past decade, the architecture, engineering, and construction industry has witnessed increasing growth in the green building market and continues to expect a promising future. Although companies in the industry have played a significant role in developing the green building market, scarce studies have analyzed changes in the market from the perspectives of these corporations. Also, few studies classify green building companies by characteristics. To fill these gaps of knowledge, we explored the characteristics of the US green building market from a corporate perspective, specifically investigating green contractors and design firms. We first collected data of 464 top US green building companies identified by the Engineering News-Record over the past decade and explored the development trend of the green building market, based on the green revenue generated by these companies. We found that the general growth trend in the US green building market in the past decade could be divided into three stages: a rapid-growth stage (2000–2008), a steady-growth stage (2009–2013), and a maturity and transformation stage (2014–2016). We then categorized the 464 organizations into three groups with distinct characteristics by k -means cluster analysis techniques, including mature green building companies, sector-oriented green building companies, and primary-stage green building companies. This study provides the academic and practitioner communities with a holistic understanding of the development of the US green building market from the view of burgeoning companies. Corporate-level management systems and sustainable practices of the three clusters of green building companies also provide guidance for construction companies in the various stages of transformation towards sustainability to adopt different strategies. [ABSTRACT FROM AUTHOR]

The Singaporean government has made building information modeling (BIM) implementation mandatory in new building projects with gross floor areas over 5000 m2, but the implementation is still plagued with hindrances such as lacking project-wide collaboration. The purposes of this study are to identify critical factors hindering BIM implementation in Singapore's construction industry, analyze their interrelationships, and identify strategies for reducing these hindrances. The results from a survey of 87 experts and five post-survey interviews in the Singaporean construction industry identified 21 critical hindrances, among which "need for all key stakeholders to be on board to exchange information" was ranked top. These hindrances were categorized into lack of collaboration and model integration (LCMI), lack of continuous involvement and capabilities (LCIC), and lack of executive vision and training (LEVT). LEVT and LCIC contributed to LCMI; LEVT caused LCIC. The proposed framework implying the key hindrances and their corresponding managerial strategies can help practitioners identify specific adjustments to their BIM implementation activities, which enables to efficiently achieve enhanced BIM implementation. The hindrances identified in this study facilitate overseas BIM implementers to customize their own lists of hindrances. [ABSTRACT FROM AUTHOR]

The relational behavior of project participants is crucial to the success of a megaproject. Although project governance has been widely studied with the aim of improving participants' relational behavior, limited research examines the distinct effectiveness of various governance mechanisms on influencing relational behavior, especially in megaprojects. Through examining three varieties of governance mechanisms—contract, trust, and institutional support—a hierarchical moderated regression analysis has been used to explore the impact of each of the governance mechanisms in facilitating the relational behavior of megaproject participants and further team performance. The analysis is based on data collected from 202 contractors and consultants working at megaprojects in China. Results unveiled that both contractual term specificity and its interaction with trust can facilitate relational behavior. Project uncertainty moderates the relationship between governance mechanisms and relational behavior, thus affecting project team performance. The findings offer both theoretical and managerial implications for megaproject participants to cultivate beneficial relational behavior to improve team performance. [ABSTRACT FROM AUTHOR]

Off-site construction (OSC) has been recognized as an approach to transform the construction sector from a labor–intensive to a modernized and green industry. Despite a number of advantages, the development of OSC still remains its infancy in China due to various interactive barriers. Some studies have been conducted to explore the barriers to the OSC adoption. However, very few studies attempted to investigate the complex interrelationships among these barriers. In order to fill this gap, this study adopts Interpretive Structural Model (ISM) technique to explore the interrelationships amongst barriers to the OSC adoption in China. Firstly, critical barriers were identified through literature review and semi-structured interviews with various stakeholders. Then, the overall structure amongst barriers was revealed through ISM technique. By using the Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) technique, the barriers were classified into four groups according to their driving-power and dependence power. The results indicate that specific attentions should be given to inadequate policy and regulations, lacking knowledge and expertise, dominated traditional project process as well as low standardization. The research findings provide valuable information for policy-makers on the overall structure amongst barriers. These results shed lights on effectively developing measures to facilitate the OSC adoption in the construction sector. [ABSTRACT FROM AUTHOR]

Considering the effects of the contractor’s conflict behaviors on the project benefit, a decision model between the owner and contractor’s conflict behaviors in construction projects was constructed using the principal-agent theory and game theory. The model was analyzed under nonconflicting and conflicting conditions, and a numerical simulation and example analysis were proposed to verify the constructed model’s conclusion. The results showed that the effort levels of the owner and contractor not only relate to benefit-sharing coefficient and effort outcome coefficient but also depend on the contractor’s ability of converting the conflict into benefit and the loss caused by conflict behaviors. A higher ability of converting conflicts into benefits and lower levels of the loss caused by conflict behaviors for the contractor lead to lower levels of the net benefit of the owner, conversely higher levels of the net benefit of the contractor. Balancing the contractor’s ability of converting conflicts into benefits and the loss caused by conflict behaviors lead to a more reasonable risk allocation between the owner and contractor, improving the effort level and net benefit. To add value to the construction project, the owner should establish an impartial and reasonable benefit-sharing mechanism, optimize the owner and contractor’s resource arrangement, maximize the positive effect of conflict on project benefits, and avoid the negative effect of conflict. Few studies to date have investigated the effects of conflict behaviors on project benefits in terms of modeling and simulation in construction projects. As such, this study bridges this gap and contributes significant theoretical and practical insights about managing conflict behaviors in an interorganizational context, thus enhancing performance in construction projects. [ABSTRACT FROM AUTHOR]

This paper aims to propose a comprehensive framework for a clear description of system boundary conditions in life cycle energy assessment (LCEA) analysis in order to promote the incorporation of embodied energy impacts into building energy-efficiency regulations (BEERs). The proposed framework was developed based on an extensive review of 66 studies representing 243 case studies in over 15 countries. The framework consists of six distinctive dimensions, i.e., temporal, physical, methodological, hypothetical, spatial, and functional. These dimensions encapsulate 15 components collectively. The proposed framework possesses two key characteristics; first, its application facilitates defining the conditions of a system boundary within a transparent context. This consequently leads to increasing reliability of obtained LCEA results for decision-making purposes since any particular conditions (e.g., truncation or assumption) considered in establishing the boundaries of a system under study can be revealed. Second, the use of a framework can also provide a meaningful basis for cross comparing cases within a global context. This characteristic can further result in identifying best practices for the design of buildings with low life cycle energy use performance. Furthermore, this paper applies the proposed framework to analyse the LCEA performance of a case study in Adelaide, Australia. Thereafter, the framework is utilised to cross compare the achieved LCEA results with a case study retrieved from literature in order to demonstrate the framework's capacity for cross comparison. The results indicate the capability of the framework for maintaining transparency in establishing a system boundary in an LCEA analysis, as well as a standardised basis for cross comparing cases. This study also offers recommendations for policy makers in the building sector to incorporate embodied energy into BEERs. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) implementation has been mandated in building projects in Singapore, but a wider adoption is still desired. This study aims to investigate the factors influencing BIM diffusion and examine how the factors influence firms with different project roles, firm sizes, and BIM implementation experience. The results of a pilot study, a questionnaire survey with 89 professionals, and five post-survey interviews showed that hindrances related to inadequate multi-party collaboration (whether formal or informal), conservative mindset, limited skills, costly infrastructure and training, and multi-discipline model integration were the most influential, whereas drivers associated with project leadership team's strategic consensus, multi-disciplinary design coordination, training, and government regulations were top-ranked. Subgroup analyses between pairs of firms with different characteristics revealed that while construction firms and less experienced stakeholders tended to underestimate BIM implementation difficulties, small-medium contractors might underestimate relevant benefits. The findings and managerial recommendations help different types of firms prioritize resources to overcome hindrances, seize opportunities (such as gaining a competitive edge from BIM practical experience), and obtain support from workers executing BIM daily. With major stakeholders' recognition and implementation, BIM can be successfully diffused in building projects and firms. The Singapore government and other countries can refer to this study when further issuing BIM diffusion policies. [ABSTRACT FROM AUTHOR]