A Bayesian Inference driven computational framework for seismic risk assessment using large-scale nonlinear finite element analyses. (December 2020) - Tadinada, Sashi Kanth; Gupta, Abhinav
Abstract: Nuclear engineers are increasingly relying on large-scale simulations particularly for seismic risk assessment. Experimentally validated simulation models are used to consider the effects of uncertainties and evaluate fragilities by conducting multiple nonlinear analyses. However, such an approach becomes computationally prohibitive and care is needed to achieve desired degree of accuracy with a reasonable amount of computational effort. In this paper, a statistical framework is presented to minimize the total computational effort needed in conducting large-scale simulations for seismic risk assessment. The salient features of the framework are: (i) use of Bayesian inference to allow consideration of data from diverse sources like experiments, field data, existing or simplified approaches, and data from large-scale simulations, and (ii) embedment of Bayesian methods within an iterative process to plan and allocate adequate computing resources such that the desired accuracy is achieved using minimum possible simulations. The applicability and efficiency of the proposed framework is illustrated using the example of a box-shaped reinforced concrete shear wall. Highlights: Simulation based seismic fragility assessment of engineering structures. A novel framework based on Bayesian inference. Incorporation of diverse data types in risk assessment such as that from experiments and high fidelity simulations. Forecast the amount of additional data that might be needed for desired accuracy. Efficient planning tool that can minimize the computational resources needed.