Recovery modeling of communities subjected to successive seismic events: Buildings and people

Major earthquakes and their cascading hazards—including aftershocks, tsunamis, and infrastructure failures—underscore the urgent need for resilient post-disaster recovery strategies. While immediate structural damage assessment has been extensively studied, the long-term functional recovery of buildings and communities subjected to successive seismic hazards remains inadequately understood. This study presents a comprehensive framework integrating probabilistic fragility functions, Monte Carlo simulations, and socio-demographic analyses to model the functional recovery of residential and commercial buildings following sequential seismic events. Building upon existing methodologies, this research incorporates earthquake-tsunami fragility surfaces and mainshock-aftershock sequences into recovery modeling, addressing financial delays and socio-economic constraints using Bayesian probabilistic approaches. Additionally, a logistic regression-based Population Dislocation Algorithm is employed to assess displacement patterns driven by structural damage and socio-economic disparities. The proposed modeling framework is validated using the Pseudo Seaside testbed, a coastal community vulnerable to multi-hazard events, enabling an in-depth analysis of both short-term repair timelines and long-term demographic shifts. Results demonstrate that sequential hazard events significantly delay recovery, with financial constraints being a primary bottleneck, disproportionately affecting lower-income households. Furthermore, aftershocks and tsunamis compound structural vulnerability, necessitating adaptive recovery policies that integrate physical, economic, and social dimensions. This research advances community resilience planning by providing a data-driven approach for quantifying functional recovery and dislocation dynamics, offering actionable insights for policymakers and emergency planners to enhance post-disaster recovery strategies in hazard-prone regions. • A probabilistic framework models functional recovery of buildings after successive seismic events. • Multi-hazard fragility surfaces integrate earthquake-tsunami and mainshock-aftershock sequences. • Bayesian probabilistic modeling quantifies financial delays and socio-economic recovery disparities. • A logistic regression-based algorithm assesses population dislocation following seismic hazards. • Results show financial constraints significantly delay recovery, impacting lower-income households.