Performance-based post-earthquake building evaluations using computer vision-derived damage observations

After a major earthquake, rapid community recovery is conditional on ensuring buildings are safe to reoccupy. Prior studies have developed statistical and machine learning-based classifiers to characterize a building's collapse capacity to resist an aftershock given mainshock responses of the building. However, for rapid safety assessment, such a method must be coupled with an automated inspection methodology to collect damage information. Furthermore, probabilistic models of expected building performance must be updated based on the distribution of observed damage. This paper presents a method for rapidly assessing the safety of a building by incorporating damage that has been identified and localized using unmanned aerial vehicle images of the building. Probabilistic models of earthquake demands on exterior components are directly updated using observed damage and Bayes' Theorem. Updated demand models on interior components are then inferred using a machine learning-based surrogate for the analysis model. Both sets of updated models are used to determine if the building is safe to occupy. Results show that predictions of building demands are improved when considering the observed damage. When combined with automated image collection and processing, the proposed methodology will enable rapid, automated safety assessment of earthquake-affected buildings.