Use of Machine Learning in Bridge Inspection and Management in Ohio

Abstract

As of 2022, the United States had 620,669 bridges that require regular inspections to ensure their safety and serviceability. These inspections are conducted in accordance with Federal Highway Administration regulations and are scheduled based on bridge condition and mandated inspection intervals. During each inspection, more than 100 bridge characteristics including location, material, geometric, and operational data are collected and used to determine the Structural Evaluation Rating (SER), a key indicator of overall bridge condition. This study investigates the application of machine learning techniques, including Pearson’s Correlation, Decision Trees, and Random Forest models, to identify and visualize relationships between bridge characteristics and the SER. The proposed methods are expected to provide rapid and reliable predictions of bridge condition based on selected characteristics and historical inspection data. In addition to improving prediction accuracy, these models offer intuitive visual interpretations of the factors influencing bridge performance. When integrated with existing inspection scheduling practices, these techniques may support more efficient and data-driven bridge management and may also assist engineers in selecting design characteristics that enhance long-term bridge performance.