Bayesian Updating and Model Class Selection of Deteriorating Hysteretic Structural Models using Seismic Response Data

Abstract

Identification of structural models from measured earthquake response can play a key role in structural health monitoring, structural control and improving performance-based design. System identification using data from strong seismic shaking is complicated by the nonlinear hysteretic response of structures where the restoring forces depend on the previous time history of the structural response rather than on an instantaneous finite-dimensional state. Furthermore, this inverse problem is ill-conditioned because even if some components in the structure show substantial yielding, others will exhibit nearly elastic response, producing no information about their yielding behavior. Classical least-squares or maximum likelihood estimation will not work with a realistic class of hysteretic models because it will be unidentifiable based on the data. On the other hand, Bayesian updating and model class selection provide a powerful and rigorous approach to tackle this problem when implemented using Markov Chain Monte Carlo simulation methods such as the Metropolis-Hastings, Gibbs Sampler and Hybrid Monte Carlo algorithms. The emergence of these stochastic simulation methods in recent years has led to a renaissance in Bayesian methods across all disciplines in science and engineering because the high-dimensional integrations that are involved can now be readily evaluated. The power of these methods to handle ill-conditioned or unidentifiable system identification problems is demonstrated by using a recently-developed stochastic simulation algorithm, Transitional Markov Chain Monte Carlo, to perform Bayesian updating and model class selection on a class of Masing hysteretic structural models that are relatively simple yet can give realistic responses to seismic loading. Examples will be given using deteriorating hysteretic building models with simulated seismic response data.

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