Xgboost application on bridge management systems for proactive damage estimation

Bridge inspection is one of the most fundamental tasks in bridge management practices. Because of limited professional manpower and budget constraints, providing prior information about possible damage can reduce inspection errors and time. The purpose of this study was to estimate the condition of bridges at a damage level, considering various influencing factors for seven different damage types by six different main structure types, using data from the Korean Bridge Management System. The extreme gradient boosting (XGBoost) method was used because it has the advantage of not assuming determinacy and independence, and it clearly can handle the numerous variables that affect damage to bridges. As a result, out of the 38 decision trees that were generated, 36 trees were derived with significant performance measures. The influence of the variables was calculated by the Shapley Additive Explanation (SHAP) value. Age, average daily truck traffic, vehicle weight limit, total length, and effective width were found to be the major factors that influenced damage to bridges. This study confirmed that more detailed structural factors were significant contributors to severe damage to complex structural designs and the use of multiple kinds of materials, such as the cross-sectional properties of girders for the concrete deck of bridges with steel girders compared to the properties of the decks for bridges made of a simple slab of reinforced concrete. The research findings emphasized the benefits of artificial intelligence in the analysis of the conditions of bridges and showed its potential for use in network-level decision making for preventive maintenance.