Seismic risk assessment using 3D physics-based seismic hazard: A case study for Shimla city

The seismic risk assessment has gained significant popularity in recent years due to the increasing development of infrastructure and urbanization in seismically active locations across the globe. Earthquakes pose serious issues as natural events because of their unpredictability and the extensive harm they may do to infrastructure, buildings, and people's lives. Ground motion at the time of the earthquake can depend on several local sites and event characteristics, such as the size of the seismic event, the depth of the earthquake focus, the distance from the epicentre, the local geology and soil conditions. However, traditional probabilistic seismic hazards using ergodic ground motion models do not consider these variations, leading to a further less accurate damage or risk assessment. Hence, the present work aims to perform a comprehensive seismic risk assessment by incorporating three-dimensional physics-based numerical modelling, which explicitly incorporates the path and site-specific characteristics that cater for non-ergodicity. Here, physics-based ground motion has been simulated for controlling events corresponding to typical sites present in Shimla city, Himachal Pradesh, India. Furthermore, to assess the associated risk for the region exposure, data of the building inventory of Shimla has been gathered using Google Street View (GSV) images, and for the classification of the building inventory to different building typologies, deep machine learning-based Convolution neural network (CNN) models are trained. The developed CNN model has shown great precision in identifying the building class for the region. After classification, suitable well-known fragility functions are mapped to each class, and subsequent risk is calculated. Finally, the results developed using physics-based hazard are compared with the conventional empirical approach. The study results will provide the respective stakeholders with the technical knowledge for the region's hazard and subsequent risk.