UCLA

Housing plays a primary role in post-earthquake recovery because all sectors of the economy rely on residents having healthy living conditions so that they will remain in the affected region. Therefore, understanding the time-dependent effects of earthquake events on housing is critical for improving post-earthquake trends through policy and planning interventions. This study develops an integrated framework for modeling post-earthquake housing recovery that combines probabilistic building performance with the decisions, actions and socioeconomic vulnerability of the affected populations. Building performance is characterized using limit states such as post-earthquake occupiability and repairability, which are explicitly linked to community functionality and recovery. Fragility functions are used to link the probability of exceeding these limit states to ground shaking intensities. Household decisions are modeled using empirical probabilistic utility models. Probabilistic discrete state models are implemented to represent post-earthquake recovery trajectories at the building, neighborhood and community scales. These models, which are inherently stochastic, can be purely empirical, such that the temporal parameters are sampled from an appropriate probability distribution. Alternatively, a simulation-based model can be employed to explicitly account for the effect of resource-availability on the time to complete relevant recovery activities. Socioeconomic vulnerability and other exogenous (external to household) and endogenous (external to household) factors are statistically linked to the temporal recovery parameters. The proposed model can be used to assist policy-makers, municipal governments and planners in understanding the possible interdependencies, interventions, and tradeoffs associated with post-earthquake housing recovery.