Seismic Ground Response Analysis Based on Multilayer Perceptron and Convolution Neural Networks

The importance of establishing a disaster prevention plan considering seismic performance is being highlighted to reduce damage to structures caused by earthquakes. Earthquake waves propagate from the bedrock to the ground surface through the soil. During the transmission process, they are amplified in a specific frequency range, and the degree of amplification depends mainly on the characteristics of the ground. Therefore, a seismic response analysis process is essential for enhancing the reliability of the seismic design. We propose a model for predicting seismic waves on the surface from seismic waves measured on the bedrock based on Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) and validate the applicability of the proposed model with Spectral Acceleration (SA). Both the proposed models based on MLP and CNN successfully predicted the seismic response of the surface. The CNN-based model performed better than the MLP-based model, with a 10% smaller average error. We plan to implement the physical properties of the ground, such as shear wave velocity, to create a more versatile model in the future.