The regeneration of abandoned industrial buildings (RAIBs) has received extensive attention in urban renewal efforts to achieve urban sustainable development goals. Meanwhile, the construction safety performance of RAIBs is a major challenge with increasing RAIB projects in China. Safety programs have been considered as one of the proactive methods to effectively reduce accidents and injuries in the construction industry. Various studies have conducted critical success factors (CSFs) that influence the effective implementation of safety programs in new buildings. However, the CSFs affecting the construction safety program implementation of RAIBs were ignored. The aim of this study is to determine CSFs that affect the safety program implementation of RAIB projects. First, sixteen factors were identified combining characteristics of RAIBs with literature reviews and experts’ opinion. Second, the fuzzy set theory and decision-making trial and evaluation laboratory (DEMATEL) approach are proposed to identify the influencing degree of the factors and categorize these factors into cause-and-effect groups. Then, according to the causal diagram, management support (C1), allocation of authority and responsibility (C3), control of subcontractor (C5), personal attitude (C9), and safety inspections and hazard assessment (C14) are identified as the CSFs for the safety program implementation of RAIBs’ construction. This study guides the managers and stakeholders to especially concentrate on these CSFs in order to improve the efficiency of the safety program implementation of RAIB projects with limited resources. This study also will contribute to the improvement of safety performance and to the sustainable development goal of RAIB projects.

Green redevelopment (GR) is a promising strategy to deal with industrial brownfields, this sustainable initiation usually fails to be implemented practically in China. Thus, investigating the driving mechanism of developer’s GR behavior, as executors of renovation project, is quite essential. The study introduced formative constructs perceived risk (PR) and perceived cost (PC), integrated them with theory of planned behavior (TPB), and extended them by adding two altruistic motives, awareness of responsibility (AR) and awareness of consequence (AC), as moderation variables to explore the bridging role of altruistic motives in GR’s intention–behavior gap. Based on 156 developers-oriented field surveys, the study conducted data analysis through partial least square structural equation modeling. It interestingly showed that subjective norm could primarily affect developers’ GR behavior, while perceived behavior control is not a significant influencing factor. Meanwhile, adding PR and PC as the additional constructs significantly increased the explanatory power of standard TPB model. Furthermore, the conclusion confirmed altruistic motives AR can distinctly adjust the relationship between GR intention and behavior, whereas AC has no such effect. These findings provide a scientific theoretical basis and a targeted path reference for promoting GR of industrial brownfields.

With the increasing exploitation and utilization of underground spaces, the excavation of deep foundation pits adjacent to existing metro tunnels is becoming increasingly common. These excavations have the potential to cause safety problems for the operation of the nearby metro. Therefore, to prevent metro tunnel accidents from occurring during the construction process and to ensure the safety of lives and property, it is necessary to establish a risk‐based early warning system. During the excavation process, the main methods for preventing accidents in excavations adjacent to existing metro tunnels are manual analyses based on on‐site monitoring data. However, these methods make it difficult to enact effective control measures in a timely manner owing to the lag of information processing. However, the trial application of artificial neural networks (ANNs) and building information modelling (BIM) for engineering projects provides a new method for solving such problems. This study uses a backpropagation neural network to predict the real‐time deformation of the tunnel based on monitoring data from the adjacent construction site. A safety risk assessment model is then established based on the relevant specifications. Through the establishment of an intelligent warning system, the safety risk to the metro tunnel during the construction process can be displayed in a three‐dimensional (3D) form using the BIM. The operation results of the ANN–BIM system show that it can effectively present the safety risk to existing metro tunnels in a 3D manner, which can provide managers with rapid and convenient visual information to inform their decision‐making.

The sustainable development of old industrial buildings is in line with the national construction strategy and has an important impact on current urban renewal. Only by achieving a unified balance among economic, social, and environmental factors can reused industrial buildings be considered sustainable. However, there are no relevant sustainability assessment indicators and methods for reused industrial buildings in China. The purpose of this study was to provide a reasonable and effective method for assessing the sustainability of reused industrial buildings. First, this study analysed the factors influencing reused industrial building sustainability through a project investigation. Second, based on the assessment indicator setting procedure, the sustainability assessment indicator system for reused industrial buildings was optimised. Moreover, a multi-level sustainability assessment model based on extenics was established to identify the correlation functions of indicators with different attributes. Finally, a case was considered to verify this assessment method. The results showed that this assessment method in good agreement with the actual state of the case was validated to be more effective and practical. The assessment method could provide a basis for decision-making to improve sustainability and could be adopted by relevant rating agencies to determine the sustainability level of reused industrial buildings.

Reuse of old industrial areas strongly impacts urban renewal and social development. However, it is limited by significant pollution and damage of buildings. In the decision‐making stage, it is necessary to conduct a reasonable vulnerability analysis of old industrial areas. This study focuses on establishing a reasonable and effective vulnerability assessment model to be used in actual work. First, based on literature analysis, a normal cloud model was employed to establish the assessment process and model. Second, foundation damage, structural bearing capacity, and other building safety indicators, as well as environmental safety, were included in the vulnerability assessment indicator system. Thus, through indicator selection and optimization, the vulnerability assessment indicators and their quantitative methods were determined and parameter features of the cloud model were programmed by the forward cloud generator. Finally, the Shougang Industrial Area, to be reused as the Winter Olympic Park, was selected as the case study. The vulnerability analysis results show that the vulnerability assessment method strongly agrees with the actual status and that the assessment results provide a basis for improving the vulnerability of old industrial areas for reuse.

The positive relationship between building performance and users’ satisfaction and behavior in new buildings has been widely studied. However, in the context of adaptively reused buildings converted from historic buildings, how each performance aspect affects users’ perception is missing. To explore the design concerns needed to be considered related to the performance of adaptively reused buildings from user’s perspective, this paper proposed a framework to examine the effect of performance of adaptively reused buildings on users’ satisfaction and behavior using structural equation modeling. A total of 608 valid face‐to‐face interview questionnaires were collected at a school building converted from an abandoned industrial building. The empirical conclusions are as follows: (1) Building performance (including indoor environment quality, interior design, building services, and authenticity) all exerted a significant positive correlation with the learning behavior of students, among which indoor environment quality is the most critical factor affecting learning behavior of students. (2) Students’ satisfaction is positively affected by the building performance (including indoor environment quality, interior design, building services, and authenticity), among which authenticity has the greatest effect on students’ satisfaction. (3) The correlation between satisfaction and learning behavior of students is significant and positive. (4) Considering the mediator impact, authenticity indirectly affects learning behavior through satisfaction, while students’ satisfaction has a partially mediating effect between the other three performance aspects and learning behavior. This paper provides a framework for assessing the performance of adaptively reused buildings and gives practical strategies for the improvement of users’ satisfaction and behavior in similar projects.