Development of a Deep Learning System for Predicting the Shape of Delamination in Rubber Dampers

Abstract

Since the 1995 Hyogo-ken Nanbu Earthquake, rubber dampers have been increasingly adopted to improve earthquake resistance. However, in recent years, a deterioration phenomenon has been reported in which the vulcanization bonding area between the bottom of the rubber damper laminate and the bottom steel plate peels off. Although this delamination causes a significant reduction in seismic performance, a method for accurately measuring the degree of delamination has not yet been established. Previous studies have shown that there is a relationship between the degree of rubber damper delamination and the shape of warpage. Therefore, in this study, we have learned this relationship by deep learning using a neural network and developed a system to estimate the degree of delamination from the shape of the rubber damper’s warpage. As a result, it was found that deep learning can be successfully used to estimate the degree of delamination from the shape of the warpage of a rubber damper.