Development of big data-driven approach for predicting bridge component deterioration: Case study in Taiwan

Aging, seismic activities, and extreme climate conditions increasingly impact key monitored national highway bridges, leading to severe deterioration and structural damage. Enhancing inspection and maintenance procedures for optimal resource allocation is a key priority for management authorities. This study analyzes 27,237 historical inspection records and employs a Random Forest (RF) model to predict deteriorating bridge component locations. The model identifies critical components requiring priority maintenance, including anti-fall devices, abutment ends, drainage facilities, and wing walls. The RF model achieves 88 % prediction accuracy, outperforming K-Nearest Neighbors (74.81 %),Support Vector Machine (78 %) and Logistic Regression (72.3 %). These results validate the RF approach for bridge deterioration classification, enabling data-driven maintenance prioritization and reducing inspection time.