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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.

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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.