A Novel Machine Learning Based Framework for Bridge Condition Analysis
Publication Date
1-1-2022
Document Type
Conference Proceeding
Publication Title
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
DOI
10.1109/BigData55660.2022.10020375
First Page
5530
Last Page
5535
Abstract
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
2100115
Funding Sponsor
National Science Foundation
Keywords
Bridge Condition Prediction, Feature Selection, Machine Leaning, National Bridge Inventory
Department
Applied Data Science
Recommended Citation
Mohammad Masum, Nafisa Anjum, Md Jobair Hossain Faruk, Hossain Shahriar, Nazmus Sakib, Maria Valero, M. A. Karim, Akond Rahman, Fan Wu, and Alfredo Cuzzocrea. "A Novel Machine Learning Based Framework for Bridge Condition Analysis" Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (2022): 5530-5535. https://doi.org/10.1109/BigData55660.2022.10020375
