Probabilistic Modeling and Inference for Building Structures under Earthquake Sequences Using Matrix-Based Bayesian Network
Publication: Lifelines 2022
ABSTRACT
To ensure the resilience of our society, the risks associated with complex natural hazards, such as a strong earthquake followed by aftershocks must be evaluated for essential structures. To handle the computational challenges caused by uncertainties in aftershock hazards, a probabilistic framework using a Bayesian network (BN) is recently proposed. In an effort to apply the BN-based framework to complex structural systems, this paper investigates a reinforced concrete (RC) flat slab building, which is a typical structural type in Mid-America. Multiple measures of structural responses including maximum inter-story drift ratios and seismic intensities of ground motions can be modeled by a BN to perform probabilistic inference based on the corresponding probabilistic relationships. To overcome the challenges that arise when handling complex systems in the BN, the overall modeling and inference processes are efficiently executed by the Matrix-based Bayesian network. The proposed framework is expected to contribute to the maintenance of a resilient community in the face of hazardous events by evaluating the risk of sequential earthquakes and making risk-informed decisions.
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Lifelines 2022
Pages: 621 - 631
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Published online: Nov 16, 2022
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