China’s construction industry is facing significant challenges in achieving sustainable development and digital operations. Integrating building information modeling (BIM) and sustainable construction is a good method for achieving these goals. However, barriers impact the applications of BIM technology to sustainable buildings, resulting in significant cost loss and time. As such, it is important to identify the constraints, hindering the application of BIM technology in sustainable buildings. This study used the factor analysis method, exploratory factor analysis (EFA), and structural equation modeling (SEM) to investigate the key constraints and conducted a questionnaire survey with 389 respondents to investigate the applications of BIM technology in sustainable building projects. The results showed that there were four main constraining factors: “Public participation”, “technology application”, “economic cost”, and “application management”; “public participation” was particularly important. The study offers practical and managerial implications based on the findings for local government and the private sector and thus can improve the implementation of BIM technology in sustainable buildings and contribute to the accomplishment of China’s sustainable development goals.

Building information modeling (BIM) is based on 3D models for collaborative management of information, which leads the innovative development of information transformation in the construction industry. In recent years, sustainable construction has become a hot topic in construction research. With the concept of sustainable development, this type of construction advocates for the sustainable optimization of construction models and processes. A person’s good behavior can bring enormous potential, which is critical for the promotion of new technology in new areas. Nowadays, the integration of BIM technology and sustainable construction has increasingly attracted attention in the industry of construction. But many issues and gaps still exist when using them. The aim of this study is to investigate the intrinsic relationship between BIM application and sustainable construction from the perspective of user application behavior, and to reveal the key influential factors of BIM application behavior in order to improve the application efficiency of BIM in sustainable construction. Specifically, the present study builds a theoretical model by integrating the theory of planned behavior and BIM application. Based on a questionnaire survey of 353 BIM users, we conducted an empirical analysis using the structural equation model method. The results showed that the perceived usefulness and ease of use of BIM technology are the key influential factors in behavior attitude. BIM behavior intention, actual behavior, and behavior attitude have a positive effect on sustainable construction. Ultimately, practical support is provided for the effective integration of BIM with sustainable construction in China.

The safety citizenship behavior (SCB) of construction workers can improve project safety performance. This study explored how construction company owners’ safety management behavior contributes to the development and encouragement of SCB. It combined the Job Demands–Resources (JD-R) and Stimulus–Organism–Response (SOR) theories to propose relevant hypotheses and develop a theoretical model to examine the effect of owners’ safety management behavior on construction workers’ SCB. Data from 534 construction workers were collected through questionnaires. Confirmatory Factor Analysis (CFA) and the Structural Equation Model (SEM) were used for empirical analysis. It was found that the owner’s safety management behavior positively affected the construction workers’ SCB. In particular, work engagement played an intermediary role, while power distance exhibited a moderating effect. A few noteworthy findings are that proactive safety behavior is significantly positively influenced by organization and coordination, prosocial safety behavior is significantly positively influenced by safety funding investment, and high power distance is not always bad for construction workers’ safety citizenship behavior. By determining the connection between owners’ safety management behavior and construction workers’ SCB, this study offered a fresh perspective on promoting construction workers’ proactive behavior and put forward suggestions for owners to improve project safety management.

The proactive safety behavior of construction workers is crucial for accident prevention. This study examines the mechanism through which altruistic leadership influences such behavior, proposing a theoretical model grounded in social exchange theory, self-determination theory, and situational strength theory. The model positions psychological empowerment as a mediator and perceived organizational support as a moderator. Hypotheses were tested using survey data from 718 construction workers in China. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by a multi-group analysis based on the workers’ age and weekly working hours. The results show that altruistic leadership significantly enhances proactive safety behavior. Psychological empowerment partially mediates this relationship, while perceived organizational support positively moderates the link between psychological empowerment and proactive safety behavior. Furthermore, the positive effect of altruistic leadership was more substantial among older workers and those with longer weekly working hours. In contrast, the mediating role of psychological empowerment was more pronounced among younger workers. These findings reveal the dual influence of internal psychological mechanisms and external contextual factors in the relationship between altruistic leadership and proactive safety behavior. This study helps managers foster safety proactivity by promoting altruistic leadership and supportive organizational environments.

Existing studies conduct general cost analyses for prefabricated components, yet structural heterogeneity results in distinct cost drivers. Most studies concentrate on the technical performance of prefabricated staircases, with insufficient investigation into dedicated cost-estimation methods. This study establishes a hybrid prediction framework integrating GAN-based data augmentation and SHAP-empowered Multilayer Perceptron (SHAP-MLP) modeling, using prefabricated straight staircases as empirical objects for multidimensional analysis. Total cost is classified into production, transportation, and on-site installation phases, followed by systematic screening of 33 influencing factors for predictive modeling. The Analytic Hierarchy Process (AHP), with a 1–9 scale, is adopted to quantify indicator weights and prioritize features. Triple verification (multi-expert consistency test, group opinion coordination test, and sensitivity analysis) removes five weakly correlated parameters to form a preliminary indicator system. Based on 240 original engineering data samples, the GAN generates 60 high-fidelity synthetic samples. Distribution consistency between synthetic and original data is validated via the Kolmogorov–Smirnov (KS) test, p-value verification, and kernel density estimation (KDE). SHAP interpretability analysis identifies four core determinants: prefabrication rate, total staircase area, standardization level, and number of floors. Eight low-impact parameters are excluded to optimize model input, leaving 20 validated indicators. The GAN-SHAP-MLP model maintains superior performance in testing, with a test-set RMSE of 49.538, representing improvements of 41.3%, 22.5%, and 25.7% over LSTM (89.33), CNN (67.59), and standard MLP (70.56), respectively. The difference between its test-set and overall R² is only 0.69%, significantly lower than 2.06% for LSTM and 5.47% for MLP. Empirical validation with real engineering cases from four different regions further confirms the model’s high prediction accuracy, with a minimum error of only 1.49%. The integration of data augmentation and interpretable deep learning provides a high-precision, interpretable cost prediction tool for prefabricated straight staircases, promoting methodological progress in construction economics.