Cities are complex systems influenced by a multitude of factors, encompassing society, economy, culture, and environment. These factors make urban development highly vulnerable to various disturbances. Communities work as the fundamental building blocks of a city and directly impact both its social structure and spatial layout. Moreover, urban planning and policies play a crucial role in shaping the development trajectory of communities and the living environment for residents. This study aims to develop a Bayesian and analytic hierarchy process (BAHP)-based multilevel community resilience evaluation method to assess the ability of the community system to withstand disturbances and recover from them. First, the proposed method establishes a comprehensive assessment index system that can evaluate social and environmental resilience as well as institutional and managerial resilience at multiple levels. This system serves as a quantitative decision-making tool to elucidate the impact of various factors on community resilience. Furthermore, the “relative demand coefficient” (RDC) is proposed to compare different communities’ resilience by using Bayesian inference to determine its most probable value (MPV). To validate the applicability of the proposed method, an empirical study was conducted in the Dafapu community located in the Longgang District of Shenzhen. Meanwhile, a simulated virtual community is employed for comparison with the Dafapu community as an illustrative example showcasing the proposed method’s superior performance after integrating the RDC. The empirical study demonstrates that the proposed BAHP-based method can effectively and quantitatively highlight the recovery capabilities and limitations for different communities in various dimensions while providing a clear direction for enhancing urban community resilience. This research contributes new insights to the theory, provides a practical tool to quantify community resilience, and offers a viable path for the actual enhancement of community resilience.

Damage identification plays a crucial role in the post-earthquake assessment and safety control of civil structures, which is usually an ill-posed inverse problem due to the presence of uncertainties and lack of measurement information. Regularization is a cutting-edge technique used to address ill-posed problems and has been developed for decades. A comprehensive review and comparison have first been conducted to identify the limitations and research gaps in the existing regularization methods for structural damage detection. Thereafter, we identified the development of the adaptive sparse regularization (ASR) method, capable of dynamically adjusting regularization parameters and sparsity according to specific damage patterns or environmental conditions, as one of the emerging research directions. Therefore, this paper systematically formulates and summarizes the theoretical framework of the ASR-based damage detection method for engineering applications to facilitate an in-depth comparative analysis. To validate the performance of the ASR method for post-earthquake structural damage diagnosis, numerical experiments are carried out on 2D and 3D models under diverse damage detection scenarios subjected to typical natural seismic excitations. These experimental investigations consider the influences of different parameter settings and uncertainties. Subsequently, the effects of damage patterns, available modal information, and solution algorithms are systematically analyzed and discussed. The results of the numerical investigation indicate that the ASR-based method is effective for damage detection, showing satisfactory accuracy and stability under complex damage scenarios and extreme conditions with a limited number of sensors and insufficient modal information. Furthermore, integrating the ASR-based damage detection method with appropriate optimization algorithms can enhance its capability to precisely identify isolated or hybrid-distributed damage.