A Condition Assessment Tool for Steel Bridge Deck Pavement Systems Based on Data Balancing Methods and Machine Learning Algorithms

Abstract

The primary challenge in the operation of steel deck pavement systems lies in the inspection and assessment of their condition. Traditionally, manual inspection methods are employed. However, these approaches are not only time-consuming and labor-intensive but also prone to human error. As a result, integrating data-driven machine learning technologies into the evaluation of pavement systems presents a significant advantage in addressing these issues. This study proposes a decision-making tool for estimating the condition levels of steel bridge deck pavement systems by employing classification techniques. To address the issue of class imbalance in the dataset, the SMOTE algorithm is utilized. Additionally, seven different machine learning methods—Light Gradient Boosting Machine, Extreme Gradient Boosting, Random Forest, Adaptive Boosting, K-Nearest Neighbor, Multilayer Perceptron, and Logistic Regression—are applied for training. Comparative analysis reveals that the Light Gradient Boosting performs optimally, achieving classification accuracies of 0.841 and 0.929 on the original and synthetic datasets, respectively.

Keywords

• steel deck pavement system
• imbalance data
• decision-making tools
• condition levels