A Bayesian Framework for Empirical Estimation of Post‐Earthquake Building Inspection Time

Abstract

Building safety inspections can be an impeding determinant of post‐earthquake recovery, yet their durations remain inadequately quantified because fine‐grained operational data are rarely available. Here, we present a probabilistic Bayesian framework to quantify building‐level inspection time, calibrated against a comprehensive multiagency dataset from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch, New Zealand. Using Markov chain Monte Carlo (MCMC) inference, we represent inspection timeframes as stochastic processes and infer marginal posterior distributions that jointly characterize aleatory variability and epistemic uncertainty in post‐disaster operations. The resulting posteriors distinguish the temporal signatures of rapid building assessment (RBA) and detailed damage evaluation (DDE) protocols and reveal strongly upper‐tailed delays driven predominantly by institutional and logistical frictions beyond damage state alone. By providing empirically derived probability curves for inspection time, the framework enhances the fidelity of regional recovery simulations and offers emergency managers an evidential basis for capacity planning, resource mobilization, and inspection strategies designed to accelerate community recovery after future earthquakes.