Study on damage level determination by deep learning in the periodic bridge inspection

Abstract

Bridges managed by Ministry of Land, Infrastructure, Transport and Tourism are required to be visually inspected once every five years. Engineers spend an enormous amount of time determining the damage level for each bridge member while looking at image data. If they can estimate the damage level automatically by using AI (artificial intelligence), they can make periodic bridge inspection record more efficiently. In this study, we constructed CNN which estimates the damage level for spalling, rebar exposure on concrete slab and clack on steel main girder by using deep learning, which is a type of AI technology with advanced image analysis ability. As a result, we constructed CNN with high-precision classification corresponding to the damage level of periodic bridge inspection guideline.