A Markov chain Monte Carlo (MCMC) simulation is proposed for probabilistic full waveform inversion (FWI) in a layered half-space. Dynamic responses on the half-space surface are estimated using the thin-layer method when a harmonic vertical force is applied. Subsequently, a posterior probability distribution function and the corresponding objective function are formulated to minimize the difference between estimations and observed data as well as that of model parameters from prior information. Based on the gradient of the objective function, a proposal distribution and an acceptance probability for MCMC samples are proposed. The proposed MCMC simulation is applied to several layered half-space examples. It is demonstrated that the proposed MCMC simulation for probabilistic FWI can estimate probabilistic material properties such as the shear-wave velocities of a layered half-space.

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This paper proposes full waveform inversion (FWI) for estimating the physical properties of a layered half-space. An FWI solution is obtained using a genetic algorithm (GA), which is a well-known global optimization approach. The dynamic responses of a layered half-space subjected to a harmonic vertical disk load are measured and compared with those calculated using the estimated physical properties. The responses are calculated using the thin-layer method, which is accurate and efficient for layered media. Subsequently, a numerical model is constructed for a layered half-space using mid-point integrated finite elements and perfectly matched discrete layers. An objective function of the global optimization problem is defined as the L2-norm of the difference between the observed and estimated responses. A GA is used to minimize the objective function and obtain a solution for the FWI. The accuracy of the proposed approach is applied to various problems involving layered half-spaces. The results verify that the proposed FWI based on a GA is suitable for estimating the material properties of a layered half-space, even when the measured responses include measurement noise.

Analytical and experimental studies show that the dynamic behavior of liquid storage tanks is significantly influenced by the fluid-structure interaction (FSI). The effects of FSI must be rigorously considered for accurate earthquake analysis and seismic design of liquid storage tanks. In this study, a dynamic analysis of a rectangular liquid storage tank subjected to bi-directional earthquake ground motions is performed and its dynamic characteristics are examined, with the effects of FSI rigorously considered. Hydrodynamic pressure is evaluated using the finite-element approach with acoustic elements and applied to the structure. The responses of the rectangular tank subjected to bi-directional earthquake ground motions are thus obtained. It can be observed that the incident angle of bi-directional horizontal ground motions has significant effects on the dynamic responses of the considered system. Therefore, the characteristics of the system must be considered in its seismic design and performance evaluation.

<p>System identification (SI) is a systematic process to estimate structural parameters by minimizing errors between measured and simulated responses of the structure. Existing SI algorithms have been suffering from ill-posedness, a well-known issue in inverse problems. To overcome challenges in SI, this paper investigates the potential use of Bayesian Network (BN) for the purpose of probabilistic identification of structural parameters. The relationships between the nodes in the BN graph are described by the conditional probability tables (CPT) obtained by Monte Carlo simulations of structural analysis. To depict the spatial distribution of deteriorating structural parameter in two-dimension effectively in a BN model, a bi- variate Gaussian function is employed. The performance of the proposed method is tested and demonstrated through comparison with the results by maximum likelihood estimation (MLE) using several assumed scenarios of structural deterioration.</p>