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The purpose of this paper is to summarize the latest research of BIM adoption in construction engineering and management (CEM) and propose research directions for future scholarly work. During the recent decade, building information modeling (BIM) has gained increasing applications and research interest in the construction industry. Although there have been review-based studies that summarized BIM-based research in the overall architecture, engineering and construction (AEC) area, there is limited review that evaluates the current stage of BIM-based research specifically in the CEM sub-area.

CEM falls into the scope of AEC. It involves construction-related tasks, activities and processes (e.g. scheduling and cost estimates), issues (e.g. constructability), as well as human factors (e.g. collaboration). This study adopted a holistic literature review approach that incorporates bibliometric search and scientometric analysis. A total of 276 articles related to BIM applied in CEM were selected from Scopus as the literature sample for the scientometric analysis.

Some key CEM research areas (e.g. CEM pedagogy, integrated project delivery, lean and off-site construction) were identified and evaluated. Research trends in these areas were identified, and analyses were carried out with regard to how they could be integrated with BIM. For example, BIM, as a data repository for ACE facilities, has substantial potential to be integrated with a variety of other digital technologies, project delivery methods and innovative construction techniques throughout the whole process of CEM.

As BIM is one of the key technologies and digital platforms to improve the construction productivity and collaboration, it is important for industry practitioners to be updated of the latest movement and progress of the academic research. The industry, academics and governmental authorities should work with joint effort to fill the gap by first recognizing the current needs, limitations and trends of applying BIM in the construction industry. For example, it needs more understanding about how to address technical interoperability issues and how to introduce the integrated design and construction delivery approach for BIM implementation under the UK BIM Level 2/3 framework.

This study contributed to the body of knowledge in BIM by proposing a framework leading to research directions including the differences of BIM effects between design-bid-build and other fast-track project delivery methods; the integration of BIM with off-site construction; and BIM pedagogy in CEM. It also addressed the need to investigate the similarities and differences between academia and industry toward perceiving the movement of BIM in construction field work.

The purpose of this paper is to address the current theoretical gap in integrating knowledge and experience into Building Information Model (BIM) for risk management of bridge projects by developing a tailored Risk Breakdown Structure (RBS) and formalising an active link between the resulting RBS and BIM.

A three-step approach is used in this study to develop a tailored RBS for bridge projects and a conceptual model for the linkage between the RBS and BIM. First, the integrated bridge information model is in concept separated into four levels of contents (LOCs) and six technical systems based on analysis of the Industry Foundation Classes specification, a critical review of previous studies and authors’ project experience. The second step develops a knowledge-based risk database through an extensive collection of risk data, a process of data mining, and further assessment and translation of data. A critical analysis is conducted in the last step to determine on which level the different risks should be allocated to bridge projects and to propose a conceptual model for linking the tailored RBS to the four LOCs and six technical systems of BIM.

The findings suggest that the traditional method and BIM can be merged as an integrated solution for risk management by establishing the linkage between RBS and BIM. This solution can take advantage of both the traditional method and BIM for managing risks. On the one hand, RBS enables risk information to be stored in a formal structure, used and communicated effectively. On the other hand, some features of BIM such as 3D visualisation and 4D construction scheduling can facilitate the risk identification, analysis, and communication at an early project stage.

A limitation is that RBS is a qualitative technique and only plays a limited role in quantitative risk analysis. As a result, when implementing this proposed method, further techniques may be needed for assisting quantitative risk analysis, evaluation, and treatment. Another limitation is that the proposed method has not yet been implemented for validation in practice. Hence, recommendations for future research are to: improve the quantitative risk analysis and treatment capabilities of this proposed solution; develop computer tools to support the solution; integrate the linkage into a traditional workflow; and test this solution in some small and large projects for validation.

Through linking risk information to BIM, project participants could check and review the linked information for identifying potential risks and seeking possible mitigation measures, when project information is being transferred between different people or forwarded to the next phase.

This study contributes to the theoretical development for aligning traditional methods and BIM for risk management, by introducing a new conceptual model for linking RBS to BIM.

This article aims to explore valuable insights into the construction progress monitoring (CPM) research domain, which include main research topics, knowledge gaps and future research themes. For a long time, CPM has been significantly researched with increasing enthusiasm. Although a few review studies have been carried out, there is non-existence of a quantitative review study that can deliver a holistic picture of CPM.

The science mapping-based scientometric analysis was systematically processed with 1,835 CPM-related journal articles retrieved from Scopus. The co-authorship analysis and direct citation analysis were carried out to identify the most influential researchers, countries and publishers of the knowledge domain. The co-occurrence analysis of keyword was assessed to reveal the most dominating research topics and research trend with the visual representation of the considered research domain.

This study reveals seven clusters of main research topics from the keyword co-occurrence analysis. The evolution of research confirms that CPM-related research studies were mainly focused on fundamental and traditional CPM research topics before 2007. The period between 2007 and 2020 has seen a shift of research efforts towards digitalization and automation. The result suggests Building Information Modelling (BIM) as the most common, growing and influential research topic in the CPM research domain. It has been used in combination with different data acquisition technologies (e.g. photogrammetry, videogrammetry, laser scanning, Internet of Things (IoT) sensors) and data analytics approaches (e.g. machine learning and computer vision).

This study provides the horizon of potential research in the research domain of CPM to researchers, policymakers and practitioners by availing of main research themes, current knowledge gaps and future research directions.

This paper represents the first scientometric study depicting the state-of-the-art of the research by assessing the current knowledge domain of CPM.

Many applications of Building Information modelling (BIM) are already integrated into project management processes. However, the construction industry is suffering from poor decision-making, especially during procurement where fundamental decisions are made. To make the best decisions at earlier project stages, such as design, large amount of information needs to be processed and classified. Therefore, this study seeks to create a Decision Support System (DSS) for construction procurement through the application of existing informatics infrastructure and BIM applications.

Literature review expert interviews and case studies with complex procurement considerations were used to identify and validate attributes and criterions for procurement decision-making. Accordingly, Multi-Attribute Utility Theory (MAUT) methodology was used and mathematical models were driven as the foundation for a DSS.

Five major criterions of time, cost, relationship quality, sustainability and quality of work performed was identified for complex construction procurement decision-making. Accordingly, a DSS structure and mathematical model was proposed. Based on this a model architecture was developed for the integration of the DSS into Autodesk Revit as a BIM platform, and assist in pre-contract decision-making.

The results can be used in pre-contract selection processes via currently used BIM applications. The model architecture can integrate DSS outputs to nD models, cloud systems and potentially virtual reality facilities to facilitate better construction operations and smarter more automated processes.

This study formulates and captures complex and unstructured information on construction procurement into a practical DSS model. The study provides a link to integrate solutions with already available platforms and technologies. The study also introduces the concept of designing for procurement; which can be expanded to other challenging decisions during construction.

The overall goal of this chapter is twofold. First, the authors aim to identify indigenous phenomena that influence employee turnover and retention in the Chinese context. Second, the authors link these phenomena to the contextualization of job embeddedness theory. To achieve the goal, the authors begin by introducing three macro-level forces (i.e., political, economic, and cultural forces) in China that help scholars analyze contextual issues in turnover studies. The authors then provide findings in the literature research on employee retention studies published in Chinese academic journals. Next, the authors discuss six indigenous phenomena (i.e., hukou, community in China, migrant workers, state-owned companies, family benefit prioritization, and guanxi) under the three macro-level forces and offer exploratory propositions illustrating how these phenomena contribute to understanding employee retention in China. Finally, the authors offer suggestions on how contextualized turnover studies shall be conducted in China.

Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

This study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.

This study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.

The findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.

This study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.

In this chapter, we estimate the Expected Shortfall (ES) in conditional autoregressive expectile models by using a nonparametric multiple expectile regression via gradient tree boosting. This approach has the advantages generated by the flexibility of not having to rely on data assumptions and avoids the drawbacks and fragilities of a restrictive estimator such as Historical Simulation. We consider distinct specifications for the information sets that produce the ES estimates. The results obtained with simulated and real market data indicate that the proposed approach has good performance, with some distinctions between the specifications.

This paper aims to investigate residential occupants’ motivations and behaviour on energy savings. Energy consumption in residential buildings is a major contributor to greenhouse gas emissions.

By using an online survey questionnaire instrument, this research collected 504 sets of responses from households in the state of New South Wales, Australia.

Through statistical analysis of the data collected, this research found that construction cost and government incentive were considered as the major influencing factors on achieving energy-efficient residential building development, and the lower bills resulted from the reduced energy and water consumption were considered as the most important benefits. The research also found that many households exhibited a high level of awareness and had implemented some sustainability improvement measures. It is suggested, based on these research findings, that governments should articulate, by means of education, the rationale and benefits of sustainable home development that are identified in this research and reduce material costs and increase government incentives.

A framework on improving residential sustainability was proposed in this paper. Stakeholders in the sustainable home supply chain could use this framework as a reference to pave the way for energy efficient home development from their perspective

This study collected the bibliographic data of 2034 journal articles published in 2000–2021 from Web of Science (WoS) core collection database and adopted two bibliometric analysis methods, namely historiography and keyword co-occurrence, to identify the evolution trend of construction risk management (CRM) research topics.

CRM has been a key issue in construction management research, producing a big number of publications. This study aims to undertake a review of the global CRM research published from 2000 to 2021 and identify the evolution of the research topics relating to CRM.

This study found that risk analysis methods have shifted from simply ranking risks in terms of their relative importance or significance toward examining the interrelationships among risks, and that the objects of CRM research have shifted from generic construction projects toward specified types of construction projects (e.g. small projects, underground construction projects, green buildings and prefabricated projects). In addition, researchers tend to pay more attention to an individual risk category (e.g. political risk, safety risk and social risk) and integrate CRM into cost, time, quality, safety and environment management functions with the increasing adoption of various information and communication technologies.

This study focused on the journal articles in English in WoS core collection database only, thus excluding the publications in other languages, not indexed by WoS and conference proceedings. In addition, the historiography focused on the top documents in terms of document strength and thus ignored the role of the documents whose strengths were a little lower than the threshold.

This review study is more inclusive than any prior reviews on CRM and overcomes the drawbacks of mere reliance on either bibliometric analysis results or subjective opinions. Revealing the evolution process of the CRM knowledge domain, this study provides an in-depth understanding of the CRM research and benefits industry practitioners and researchers.