Efficient Decision Tree-Based Classification Models to Predict Safety Rating for Bridge Maintenance

Abstract

To address insufficient costs and manpower available for maintenance of aging bridges, recent research has been examining advanced maintenance technologies that can theoretically predict the condition and performance of infrastructure facilities. The current study proposes a method that is intended to predict the safety rating of bridges; among the various machine learning techniques available for this purpose, a decision tree-based classification model has been selected. Using decision tree, random forest, XGBoost (extreme gradient boosting), and LightGBM (light gradient boosting machine), 8,850 bridges on general national roads in Korea were analyzed, and the results were compared. It was possible to identify the variables that have critical impacts on the model during the model formation process. The models were analyzed through various evaluation metrics or indices such as balanced accuracy, recall, ROC (receiver operating characteristic) curve, and AUC (area under the curve). The results showed that the models using random forest, XGBoost, and LightGBM, and not those using a decision tree, exhibited excellent performance in predicting bridge safety ratings. These models achieved a recall of more than 80% for bridges with C and D ratings, which are the main targets of maintenance due to their high degree of aging. Moreover, the AUC exceeded 0.8, indicating that the prediction of bridges with ratings other than C and D was also satisfactory. These results indicate that the multiclass classification model applied in this study, with proper data sampling technique and the optimum parameters, showed improved predictive performance compared with the existing models. As infrastructure throughout the world—such as bridges—continues to age, proper maintenance is becoming increasingly important. However, with the increasing number of aging bridges that require such consideration, it is increasingly common for there to be insufficient costs and manpower available for maintenance. As a result, there have been several bridge collapses due to lack of maintenance, such as the I-35W Mississippi River Bridge, Ynys-y-Gwas Bridge, and Malle Bridge. The proposed multiclass classification model using a decision tree is expected to contribute to the establishment of proactive and economical bridge maintenance plans by allowing for quick identification of the safety rating of bridges without available data on safety inspections and the estimation of a bridge’s safety rating at a specific time. Therefore, this study can contribute to bridge asset management (BAM) by determining an optimal time for maintenance to prolong the asset’s life.