Design of the base isolation system with artificial neural network models

Abstract

This work presents the application of the artificial neural networks (ANN) for modeling and designing Seismic-Isolation (SI) systems consisting of Natural Rubber Bearings and Viscous Fluid Dampers subject to Near-Field (NF) earthquake ground motion. Four lumped-mass stick models representing a realistic five, ten, fifteen, and 20-story base-isolated buildings are used. The key response parameters selected to represent the behavior of SI system are the Damper Force (PDF), Total Maximum Displacement (DTM), the Peak the Top Story Acceleration Ratio (TSAR) of the isolated structure compared to the fixed-base structure and the maximum amplified drift ratio (δmax). Twenty-four NF earthquake records representing two seismic hazard levels are used. The commercial analysis program SAP2000 was used to perform the Time-History Analysis (THA) of the MDOF system (stick model representing a realistic N-story base-isolated building) subject to all 24 records. Different combinations of damping coefficients (c) and damping exponents (a) are investigated under the 24 earthquake records to develop the database of feasible combinations for the SI system. The total number of considered THA combinations is 751680 and were used for training and testing the neural network. Mathematical models for the key response parameters are established via ANN and produced acceptable results with significantly less computation. The results of this study show that ANN models can be a powerful tool to be included in the design process of Seismic-Isolation (SI) systems, especially at the preliminary stages.