A Novel Machine Learning Based Framework for Bridge Condition Analysis

Publication Date


Document Type

Conference Proceeding

Publication Title

Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022



First Page


Last Page



Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country's social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country's 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score.

Funding Number


Funding Sponsor

National Science Foundation


Bridge Condition Prediction, Feature Selection, Machine Leaning, National Bridge Inventory


Applied Data Science