Matthew M. Muto

TL;DR: It is shown here that Bayesian updating and model class selection provide a powerful and rigorous approach to tackle the problem of hysteretic system identification when implemented using a recently developed stochastic simulation algorithm called Transitional Markov Chain Monte Carlo.

TL;DR: A new Bayesian model updating approach for linear structural models based on the Gibbs sampler, a stochastic simulation method that decomposes the uncertain model parameters into three groups, so that the direct sampling from any one group is possible when conditional on the other groups and the incomplete modal data.

TL;DR: In this paper, the authors explored the mechanism of collapse of tall steel moment-frame buildings under earthquake excitation and showed that only long-period excitation imparts energy to tall buildings large enough to cause collapse.

TL;DR: In this paper, the authors developed one plausible realization of the effects of the scenario event on tall steel moment-frame buildings, and used the simulated ground motions with three-dimensional nonlinear finite element models of three buildings in the 20-story class to simulate structural responses at 784 analysis sites spaced at approximately 4 km throughout the San Fernando Valley, the San Gabriel Valley, and the Los Angeles Basin.

TL;DR: In this paper, the Gibbs sampler is used to decompose the uncertain stiffness and mass parameters of a linear structural model into three groups, such that the sampling from any one group is possible when conditional on the other groups and the modal data.