Urban infrastructure (UI), subject to ever-increasing stresses from artificial activities of human beings and natural disasters due to climate change, assumes a key role in modern cities for maintaining their functional operations. Therefore, understanding UI resilience turns essential. Based on the Pressure-State-Response (PSR) model, this paper built a comprehensive evaluation index system for urban infrastructure resilience evaluation. Four municipalities, including Beijing, Tianjin, Shanghai, and Chongqing in China, were selected for the case study, given their specific significance in terms of geographical location and urban infrastructure scale. Temporal differences of UI resilience in those four cities during 2002–2018 were explored. The results showed that: (1) The various stages of PSR relative importance for the urban infrastructure resilience development in the four cities were different. The infrastructure status, primarily resource environmental benefit, had the most significant effect on urban infrastructure resilience, accounting for 38.73%. (2) While Shanghai ranked first, the levels of urban infrastructure resilience in four cities were generally poor in 2002–2018 with continuously low resilience. (3) Significant differences were found in the resilience levels associated with the three stages of pressure, state and response failing to form a positive development cycle, with the poorest pressure resilience. This paper puts forward some recommendations for providing scientific support for urban resilient infrastructure development in four municipalities in China.

In recent years, the rise of the domestic industry has boosted the use and popularity of prefabricated buildings. Prefabricated buildings differ significantly from traditional design, construction, and production models. However, due to the short development period of prefabricated buildings in China, the quality management of these new structures is still not mature, resulting in frequent project delays and failure. To improve quality management, this paper aims to establish an evaluation model of factors affecting prefabricated building quality. The 4M1E framework was used to categorize and generalize related quality factors. Then, GeNIe software was used to establish a visual Bayesian network quality factor evaluation model. The factors that need to be managed and given attention to in the prefabricated construction project were discovered using reverse reasoning, sensitivity, and critical factor analysis. The results indicated that among the multiple stages of prefabricated buildings, the construction stage has the greatest impact on the quality of buildings. C2(Insufficient sense of responsibility of construction personnel) is the most significant factor that needs to be controlled. In addition, this paper combined the ISM-BN model with actual engineering projects to identify key factors affecting the project’s quality, demonstrating the model’s applicability. The evaluation model of quality factors in prefabricated buildings was established. It can identify the underlying causes of quality issues in prefabricated buildings and control engineering quality at the source, acting as an effective guide for practitioners and enterprises.