Infra Machine Learning for Predictive Maintenance via Classification Models

Abstract

Aging infrastructure that was developed during the period of high growth is beginning to become obvious. Visual inspection and sensing are carried out to monitor the state of deterioration in preventive maintenance. It is the standard procedure in Japan that bridges with important structures are inspected once in five years where they visually checks the proximity. A high accuracy and efficiency improvement of the bridge inspection is an urgent issue for the both local public municipalities and private enterprises. This paper proposes multi-grade classification models to visualize the weights of feature variables that aim the countermeasure. In addition, the author proposes a method of sensitivity analysis on deterioration progression in the future in response to unit change of degradation factors. Indeed, these methods applied to a bridge inspection data such as super structure steel corrosion and deck reinforced concrete crack