ESTIMATION AND ANALYSIS OF BRIDGE DAMAGE CAUSES AND REPAIR METHODS USING GRADIENT BOOSTING DECISION TREE

Abstract

Gradient Boosting Decision Tree (GBDT), a machine learning technique, is often used in data analysis competitions because of its superior accuracy and computational speed. The purpose of this study is to investigate the applicability of GBDT for estimating the cause of damage and the repair method from the information of bridge management records stored in a bridge management system, in order to assist the decision-making of road administrators in local governments in selecting the repair method.

As a result, GBDT is capable of estimating the cause of damage with high accuracy for all models. In addition, the influence of the specifications on the cause of damage is analyzed by using importance and SHAP values of the explanatory variables.