Explainable Deep Learning for Bridge Repair Decision

Abstract

The basic policy of bridge management is preventive maintenance which contains to execute bridge inspection, diagnosis, repair design, and records continuously. Deep learning which has been widely used in bridge inspection is not well examined in repair design because it is more complex and requires explainability. This study aims to propose a bridge repair decision model consisting of a damage grade assessment model and a damage progress decision model for repair design. The model trained by using periodic inspection data including images and texts achieved about 0.7 recall rate in bridge repair decision task.