Extraction of Deterioration Factors from Bridge Inspection Data Using AI and Their Application to Maintenance

Abstract

This study constructed an integrated dataset centered on the nationwide road facility inspection database (xROAD), combining structural information, meteorological data, and maintenance records, and applied AI methods to analyze bridge deterioration factors. For steel bridges, the damage score per unit area (Saa) was introduced, and explainable machine learning models incorporating principal component analysis (PCA) and SHAP were employed to clarify the key features influencing corrosion progression. For concrete bridges, inspection documents, specifically the “Comprehensive Inspection Results” section, were analyzed using morphological analysis and tf-idf, revealing the relationship between textual information and changes in bridge condition.