Extremely heavy rainfall has posed a significant hazard to urban growth as the most common and disaster-prone natural calamity. Due to its unique geographical location, the metro system is more vulnerable to waterlogging caused by rainstorm disaster. Research on resilience to natural disasters has attracted extensive attention in recent years. However, few studies have focused on the resilience of the metro system against rainstorms. Therefore, this paper aims to develop an assessment model for evaluating metro stations’ resilience levels. Twenty factors are carried out from dimensions of resistance, recovery and adaptation. The methods of ordered binary comparison, entropy weight and cloud model are proposed to build the assessment model. Then, taking Chongqing metro system in china as a case study, the resilience level of 13 metro stations is calculated. Radar charts from dimensions of resistance, recovery, and adaptation are created to propose recommendations for improving metro stations’ resilience against rainstorms, providing a reference for the sustainable development of the metro system. The case study of the Chongqing metro system in china demonstrates that the assessment model can effectively evaluate the resilience level of metro stations and can be used in other infrastructures under natural disasters for resilience assessment.

Effective waste management is an important aspect of green building development. However, a number of studies find that construction waste minimization actions are not satisfactorily performed in practice. Thus, it is of significance to identify the driving factors that influence construction practitioners' waste minimization behavior. This paper aims to explore the driving factors by comparing the affecting factors and construction waste minimization behavior between Hong Kong and Shenzhen. Firstly, through a comprehensive literature review, potential affecting factors were categorized into five groups: background information variables, construction waste minimization intentions, economic viability, governmental supervision, and project constraints. Based on the identified factors, a questionnaire was initially designed and then refined through a focus group meeting with three experienced experts. A total of 166 questionnaires were collected and non-parametric tests (Mann-Whitney U Test and Kruskal-Wallis Test) were conducted to investigate the underlying driving factors. Results revealed that background information variables play insignificant roles in construction waste minimization. Compared with other affecting factors (i.e., waste minimization intentions, governmental supervision, project constraints), economic viability is the most important driving factor that determines construction waste minimization. Results suggested that local governments should make policies that increase practitioners' profit, rather than just increasing their intentions or regulating waste management behavior.

Improving the ability of the urban rail transit system to cope with rainstorm disasters is of great significance to ensure the safe travel of residents. In this study, a model of the hierarchical relationship of the influencing factors is constructed from the resilience perspective, in order to research the action mechanisms of the influencing factors of urban rail transit stations susceptible to rainstorm disaster. Firstly, the concept of resilience and the three attributes (resistance, recovery, and adaptability) are interpreted. Based on the relevant literature, 20 influencing factors are discerned from the 3 attributes of the resilience of urban rail transit stations. Subsequently, an interpretative structural model (ISM) is utilised to analyse the hierarchical relationship among the influencing factors. The key influencing factors of station resilience are screened out using social network analysis (SNA). Combined with ISM and SNA for analysis, the result shows that the key influencing factors are: “Flood prevention monitoring capability”; “Water blocking capacity”; “Flood prevention capital investment”; “Personnel cooperation ability”; “Emergency plan for flood prevention”; “Flood prevention training and drill”; “Publicity and education of flood prevention knowledge”; and “Regional economic development level”. Therefore, according to the critical influencing factors and the action path of the resilience influencing factors, station managers can carry out corresponding flood control work, providing a reference for enhancing the resilience of urban rail transit stations.