Probabilistic Seismic Risk Assessment of a reinforced concrete building considering hazard level and the resulting vulnerability using Bayesian Belief Network

Seismic losses have significantly increased in size and frequency during the past few years, harming the economy and communities. Seismic Risk Assessment (SRA) requires integration of seismic hazard, building exposure and vulnerability, which entails many levels of complicated models, and necessitates taking uncertainties into account. The present study focuses on the probabilistic SRA of a single building, thereby excluding building exposure modelling and incorporating only seismic hazard and the resulting building vulnerability. In this study, the confidence level on probabilistic SRA is enhanced by considering a soft computing technique like Bayesian Belief Network (BBN) that makes use of the strength of Bayesian statistics to account for complicated connections and correlations amongst events at different levels of a network model of the system. This approach is based on developing a node-based model and assigning probabilities to each node by forming a Conditional Probability Table (CPT), which is based on both data as well as logically driven assumptions. The probabilistic seismic risk obtained has been represented in the form of three indices: low, medium and high. The established BBN model is next subjected to sensitivity analysis, which can help with the evaluation of updated data as new information from experimental observations or improved simulations is integrated. The application of the methodology is illustrated for a reinforced concrete (RC) hospital building located at Silchar city in northeast India, which is one of the most seismically active regions of the country. The developed model enables the identification of the seismic risk associated with a particular building which can be utilised to guide stakeholders, policymakers and designers in the efficient planning of emergency response, rescue operations and recovery activities.