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This study aims to investigate the literature related to the use of digital technologies for promoting circular economy (CE) in the construction industry.

A comprehensive approach was adopted, involving bibliometric analysis, text-mining analysis and content analysis to meet three objectives (1) to unveil the evolutionary progress of the field, (2) to identify the key research themes in the field and (3) to identify challenges hindering the implementation of digital technologies for CE.

A total of 365 publications was analysed. The results revealed eight key digital technologies categorised into two main clusters including “digitalisation and advanced technologies” and “sustainable construction technologies”. The former involved technologies, namely machine learning, artificial intelligence, deep learning, big data analytics and object detection and computer vision that were used for (1) forecasting construction and demolition (C&D) waste generation, (2) waste identification and classification and (3) computer vision for waste management. The latter included technologies such as Internet of Things (IoT), blockchain and building information modelling (BIM) that help optimise resource use, enhance transparency and sustainability practices in the industry. Overall, these technologies show great potential for improving waste management and enabling CE in construction.

This research employs a holistic approach to provide a status-quo understanding of the digital technologies that can be utilised to support the implementation of CE in construction. Further, this study underlines the key challenges associated with adopting digital technologies, whilst also offering opportunities for future improvement of the field.

Building information modeling (BIM) is recognized as one of the technologies to upgrade the informatization level of the architecture engineering and construction (AEC) industry. However, the level of BIM implementation in the construction phase lags behind other phases of the project. Assessing the level of BIM implementation in the construction phase from a system dynamics (SD) perspective can comprehensively understand the interrelationship of factors in the BIM implementation system, thereby developing effective strategies to enhance BIM implementation during the construction phase. This study aims to develop a model to investigate the level of BIM implementation in the construction phase.

An SD model which covered technical subsystem, organizational subsystem, economic subsystem and environmental subsystem was developed based on questionnaire survey data and literature review. Data from China were used for model validation and simulation.

The simulation results highlight that, in China, from 2021 to 2035, the ratio of BIM implementation in the construction phase will rise from 48.8% to 83.8%, BIM model quality will be improved from 27.6% to 77.2%. The values for variables “BIM platform”, “organizational structure of BIM” and “workflow of BIM” at 2035 will reach 65.6%, 72.9% and 72.8%, respectively. And the total benefits will reach 336.5 billion yuan in 2035. Furthermore, the findings reveal five factors to effectively promote the level of BIM implementation in the construction phase, including: policy support, number of BIM standards, owners demand for BIM, investment in BIM and strategic support for BIM.

This study provides beneficial insights to effectively enhance the implementation level of BIM in the construction phase. Meanwhile, the model developed in this study can be used to dynamically and quantitatively assess the changes in the level of BIM implementation caused by a measure.

Prefabricated construction (PC) will play a vital role in the transformation and upgrading of the construction industry in the future. However, high capital cost is currently one of the biggest obstacles to the application and promotion of PC in China. Clarifying the factors that affect the PC cost from the perspectives of stakeholders and exploring key cost control paths help to achieve effective cost management, but few studies have paid enough attention to this. Therefore, this research aims to explore the critical cost influencing factors (CIFs) and critical stakeholders of PC based on stakeholder theories and propose corresponding strategies for different stakeholders to reduce the cost of PC.

Based on the stakeholder theory and social network theory, literature review and two rounds of expert interviews were used to obtain the stakeholder-associated CIFs and their mutual effects, then the consistency of the data was tested. After that, social network analysis was applied to identify the critical CIFs, critical interaction and key stakeholders in PC cost control and mine the influence conduction paths between CIFs.

The results reveal that the cognition and attitude of developer and relevant standards and codes are the most critical CIFs while the government, developer and contractor are crucial to the cost control of PC. The findings further suggest that measures should be taken to reduce the transaction costs of the developer, and the contractor ought to efficiently apply information technology. Moreover, the collaborative work between designer and manufacturer can avoid unnecessary cost consumption.

This research combines stakeholder management and cost management in PC for the first time and explores the effective cost control paths. The research results can contribute to clarifying the key points of cost management for different stakeholders and improving the cost performance of PC projects.

Sustainability and competitiveness have received extensive attentions. Despite a large number of studies on sustainability and competitiveness in the construction industry, little research has been conducted to holistically explore the interactions between these two concepts. From a dynamic transition perspective, the purpose of this paper is to link sustainability and competitiveness of construction firms by developing a Sustainability-Competitiveness Dynamic Interaction Framework (SCDIF).

Conceptual theory-building approach was adopted to develop the conceptual framework. It is an iterative analysis and synthesis process, which involves reading literature, identifying commonalities and differences, synthesizing, proposing an initial framework, collecting additional literature, and revisiting and revising the framework.

There are complex interactions between sustainability and competitiveness of construction firms. This leads to uncertain relationships between sustainability and competitiveness, which is context dependent. Under evolving economic and socio-political environments, sustainability and competitiveness of construction firms could transition from mutually exclusive to mutually supportive, and finally merge into “sustainable competitiveness.”

A SCDIF proposed in this study demonstrates that the interactions between sustainability and competitiveness evolves according to the evolving economic and socio-political environments and firms’ strategies, and thus the relationships and interactions between sustainability and competitiveness are context dependent. This framework helps corporate managers to understand how corporate sustainability and competitiveness interact with each other, thereby informing their decision-making of sustainability strategy. Similarly, the framework provides useful references for policymakers to understand the mechanisms of transitioning industries toward sustainable competitiveness.

The proposed framework offers a new perspective for understanding sustainability and competitiveness. From the dynamic transition perspective, this study effectively illustrates that the interactions between sustainability and competitiveness evolves according to the evolving economic and socio-political environments and firms’ strategies. Compared to existing approaches, the dynamic and holistic approach proposed in this paper provides the capacity to capture the complexity of sustainability and competitiveness.

This study explores the potential of digital twin (DT) technology to enhance education and training in the construction industry. It aims to provide a clear understanding of how DT can be applied for educational purposes and proposes a framework to facilitate the adoption of DT in construction training.

A systematic literature review was conducted to examine the current applications of DT technology in construction education and training. A total of 19 relevant studies were identified and analysed to evaluate the tools, technologies, educational objectives and integration methods used in developing DT models for the construction sector. Based on this analysis, a conceptual framework was developed to guide the integration of DT technology into construction education, addressing gaps in the current literature and practices.

The analysis revealed a strong consensus on the effectiveness of DT technology in supporting education and training objectives within the construction industry. The study highlighted the fragmented nature of the current literature and proposed a comprehensive framework designed to facilitate the integration of DT in construction education. This framework offers a structured approach to bridging the gap between theoretical learning and real-world application.

The research presents a new systematic framework developed based on an in-depth review for utilising DT in education, training and learning (ETL) processes in construction. The framework provides a novel and structured learning process to integrate theoretical knowledge with practical skills to support workforce development in the construction industry. This framework offers a structured roadmap for future research and practical applications.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The purpose of this study is to analyze the determinants of intention for energy and water conservation behavior in Prishtina, Kosovo by using the theory of planned behavior (TPB) conceptual framework and then examine the influence of intention and demographic factors on the conservation behavior itself. In addition, the present study examines the differences between urban and rural consumers in Prishtina in terms of their intention for energy and water conservation behaviors and their actual conservation behavior.

This study uses a qualitative approach by conducting ten in-depth interviews followed by one focus group with urban consumers and ten in-depth interviews followed by one focus group with rural consumers in Prishtina to analyze the influence of determinants on the conservation intention. In addition, the present study uses the quantitative research method to empirically examine the influence of intention and demographic variables on the actual conservation behavior.

The findings show that there is a difference between the urban and rural sample populations in Prishtina in terms of determinants that influence their intention to conserve energy and water. While attitude is the strongest determinant among the urban population, the social norms seem to be the strongest antecedent of the behavioral intention among the rural population. In addition, the study finds that the intention, income, family size and place of residence as a whole influence the actual behavior; however, the manifestation of the influence of separate variables on the actual conservation varies between urban and rural population. While intention is very strong among urban respondents and the actual conservation behavior is less dependent on the income level and family size, in the case of rural respondents, intention alone is not sufficient to predict the actual behavior and varies also on the income level.

The study brings unique and new knowledge about the application of the TPB in the context of small and developing economies bridging the research gaps arising from few scholarly research studying the differences between urban and rural populations.