Modelling Seismic Performance of Reinforced Concrete Buildings Within Response Spectrum Framework

This paper deals with the modelling and analysis of reinforced concrete buildings for seismic performance within the response spectrum framework using a deep learning toolbox in 64-bit MATLAB R2021a. The response of a building subjected to earthquake ground accelerations is of paramount importance for designing earthquake resistant structures. Huge loss of life and property has resulted in extensive research in the field of seismic prediction and analysis for accurate results. Artificial Intelligence (AI) and Machine Learning (ML) techniques are thus finding a wide variety of applications in seismic analysis for gaining new insights. The seismic data available has increased exponentially in its size, thus AI has emerged as the solution for this challenging task of processing such overwhelming time-history earthquake data sets. The response spectrum method of seismic analysis is widely used as it computes peak displacements and member forces. In the present work, ground motion recordings of the El Centro earthquake, one of the most studied earthquake data is considered as the input data sets along with two other earthquakes of the Indian subcontinent, namely, the Bhuj earthquake and the India–Myanmar earthquake. The response spectrums are developed for multi degrees of freedom (MDOF) systems based on Newmark’s method for linear systems. The ground acceleration data of the three earthquake records are used as inputs and the peak displacement, base shear and strain energy are computed. Numerical examples presented illustrate the effectiveness of the deep learning toolbox in MATLAB for determining the seismic performance of reinforced concrete buildings.