基于有限震后传感数据的区域地震损失概率分析与抗震韧性评估方法OA

Post-earthquake regional-scale seismic loss and resilience assessment under limited sensor deployment remains highly uncertain.Effectively integrating sparse post-earthquake monitoring data with pre-earthquake simulation results to reduce prediction uncertainty remains a major technical challenge.To address this issue,a probabilistic framework for regional seismic loss analysis and resilience assessment based on limited post-earthquake sensor data was proposed,where post-earthquake regional loss distributions were updated by combining pre-earthquake prior loss probability distributions with sparse sensor observations.A scenario earthquake with a moment magnitude of 7.0 in the target region was considered,and regional seismic loss distributions were updated under different sensor coverage levels.The results indicate that,as sensor coverage rate increases from 0%to 90%,the mean estimates of post-earthquake repair cost and repair time remain stable,while extreme loss scenarios are reduced;meanwhile,the variances of repair cost and repair time decrease by approximately 56%and 46%,respectively,and the corresponding coefficients of variation decrease by about 27%and 29%.The case study verifies the critical role of post-earthquake sensor information in reducing assessment uncertainty and demonstrates that the proposed method effectively fuses limited observations with prior simulated loss distributions while preserving ground-motion spatial correlation,thereby enhancing the reliability of regional seismic loss and resilience assessment and supporting post-earthquake emergency response and resource allocation.