Towards Seismic Damage Assessment Model for Soil Structure Interaction-based Building

Abstract Buildings located in seismic risk areas are susceptible to damage, so evaluation of the seismic performance of building structures is essential for accurate seismic design. Seismic Damage Assessment (SDA) is a difficult task for individual building structures dealing with FiniteElement Analysis (FE) and simulation techniques are time-consuming due to the complex model. Moreover, computational techniques based on FE with Soil-Structure Interaction (SSI) for earthquake damage assessment of buildings require high computational efforts to build the database for the development of the area-based prediction model. Therefore, this study provides a framework for an artificial neural network (ANN)-based model as a reliable alternative to developing a rapid decision-making tool for building SDA. To establish the SDAbased ANN model, three inputs, including seismic, building, and soil were selected as the main parameters, and seismic responses with soil-structure interaction were generated using a multi-step analysis process proposed. The results obtained demonstrate the high accuracy of the SDA-Net model proposed as a predictive approach and rapid decision support tool for structures with SSI impacts as a predisaster management approach for the assessment of damage and a handy tool for safe and durable structures against natural disasters.