This paper analyzed the factors that influence the seismic resilience of urban building groups and studied the laws that influence internal factors and external factors. Based on the data from the first national comprehensive risk survey of natural disasters, a refined classification study of urban building groups was carried out. Based on the existing evaluation methods of seismic resilience of individual buildings, the recovery time was selected as the resilience evaluation index to calculate the effect of internal factors on the seismic resilience of urban building groups. Then, we studied the quantitative relationship between external factors (i.e., disaster relief capacity, population density, and economic level) and the evaluation indicators of seismic resilience of urban building groups, and we proposed the kilometer grid coefficient. Based on that, we proposed a calculation method of the effect of external factors on the seismic resilience of urban building groups. Considering the influence of internal and external factors, the evaluation model for the seismic resilience of urban building groups was established. And the model was applied in a typical city. This paper proposes a method to evaluate the seismic resilience of urban building groups, which can master the functional recovery time of urban building groups after an earthquake. Based on the proposed model, we can optimize the functional recovery path and emergency rescue path of the disaster area, as well as improve the resilience of urban building systems and the construction of resilient cities.

In order to meet the demand for the seismic-resilience assessment of urban–rural building clusters, a new classification method is proposed by integrating national risk census results and evaluated using a large-scale dataset. This study initially identifies and analyzes the key factors that influence seismic-resilience. The reason for considering both internal and external factors is that they comprehensively reflect the characteristics and influencing conditions of urban–rural building clusters in terms of seismic-resilience. Subsequently, a comprehensive evaluation index system is constructed, encompassing both internal and external factors. Based on this system, criteria for seismic-resilience grading are proposed to classify the resilience levels of different building clusters. This is crucial for differentiating the seismic resilience capabilities of various building clusters. The evaluation index weights are determined by means of a robust method, and a seismic resilience evaluation method for urban–rural building clusters is established on the basis of the fuzzy comprehensive evaluation theory. This method incorporates various internal and external influencing factors to offer a comprehensive assessment. Moreover, by leveraging the ArcGIS platform, the evaluation method is successfully applied to urban–rural building clusters. Taking Weinan City, China, as a case study, an empirical evaluation of the seismic resilience of Weinan City is carried out. The results indicate that the proposed method effectively reflects the seismic resilience of the building clusters and offers valuable insights for enhancing resilience. The research findings provide a solid theoretical foundation and practical reference for enhancing the seismic resilience of urban–rural building clusters, promoting resilient city construction, and supporting post-earthquake disaster-area recovery and reconstruction.

This study presents a multi-factor coupled assessment of seismic disaster risk for approximately 635,000 individual building units in Ankang City, Shaanxi Province, China, utilizing a high-resolution dataset. The assessment methodology innovatively integrates the three core components of risk: seismic vulnerability V of load-bearing structures, site-specific seismic hazards R, and potential consequences C of damage, to formulate the Seismic Resilience Index I_SR = C·R·V. Crucially, the approach advances established risk assessment frameworks by enhancing the spatial resolution of the site influence coefficient R using a high-resolution national site classification map and detailed local geological data. The results reveal that the areas with the lowest I_SR values (indicating the lowest resilience and thus the highest risk) are predominantly concentrated in older residential districts of counties such as Ningshan, Hanyin, and Ziyang, where unreinforced masonry structures built prior to 1989 are widespread. The model assessment results align with expected structural performance characteristics, and the study concludes by offering quantified, priority-based recommendations for targeted structural intervention and seismic retrofitting in the identified highest-risk regions and building typologies.