Metamodel-based state-dependent fragility modeling for Markovian sequential seismic damage assessment: Fid-Bau Portal

Metamodel-based state-dependent fragility modeling for Markovian sequential seismic damage assessment

Du, Ao / Cai, Jiannan / Li, Shuai

Highlights • Parameterized state-dependent seismic fragility modeling with damage accumulation. • Impact of predictor selection on state-dependent fragility and time-dependent risk. • Impact of time increment selection on the sequential seismic damage assessment. • Discussion on pre- and post-event sequential seismic damage assessment.

Abstract Accurate characterization of seismic damage accumulation is crucial to reliable seismic risk assessment. Emerging research efforts have been devoted to state-dependent fragility modeling considering the impact of sequential earthquake excitations so as to better inform the management of structure and infrastructure assets. However, the current state-dependent fragility models largely rely on simple functional forms conditioned on only a scalar ground motion intensity measure (IM); fragility models are separately developed for different state-transition scenarios, without any joint modeling mechanism to preserve the multivariate nature among the different damage states; and the lack of parameterization further hinders these models from offering pertinent fragility estimates for structural portfolios with uncertain structural parameters. By leveraging advanced ground motion selection and machine learning techniques, this study proposes a novel metamodel-based state-dependent fragility modeling approach that offers superior parameterization and damage classification capability and can be seamlessly integrated into the Markovian sequential seismic damage assessment (SSDA) framework. The proposed fragility modeling approach features a systematic sequential dynamic analysis procedure that leverages efficient experimental design and hazard-consistent ground motion selection to sufficiently cover the feature space, and an artificial neural network (ANN) classification model conditioned on one-hot encoded initial damage states along with other earthquake and structure-related predictors. Based on generic inelastic single-degree-of-freedom (SDOF) systems with varying structural configurations, damage state classification performance of the proposed fragility model is thoroughly evaluated, and its applicability within the Markovian SSDA framework is elaborated and discussed.