An application of convolutional neural network for deterioration modeling of highway bridge components in the United States

Abstract

This paper presents a deep learning-aided deterioration modeling approach for highway bridge components (i.e., decks, superstructures, and substructures). The method trains a set of deep learning models for the maximum likelihood estimation of parameters in a Markov chain. The likelihood function is based on observed numbers of transitions between different condition states. The deep learning is leveraged for efficient representation of various factors that influence the deterioration process. Aided by deep learning, the proposed method is suitable for deterioration modeling using large-scale and high-dimensional bridge datasets. The study demonstrates an application of this approach using the historical National Bridge Inventory database from 1993 to 2019. The Convolutional Neural Network is adopted as the deep learning model. The proposed method is validated with ten-fold cross-validation that encompasses a nationwide selection of 88,596, 101,414, and 96,244 bridge decks, superstructures, and substructures, respectively. The validation shows this approach achieved a robust and low prediction error with the maximum mean-squared-error near 0.5 in a 26-years-forecast. The proposed method is also compared with the conventional Artificial Neural Network and four selected Markov-chain based deterioration modeling approaches. The study shows this approach can be a promising data-driven tool for deterioration modeling of highway bridge components.

Keywords:

• National bridge inventory
• bridge condition rating
• deep learning
• convolutional neural network
• condition forecast
• deterioration modeling
• Markov chains

Acknowledgements

This research was performed while the first author held a National Research Council (NRC) Research Associateship award at the Turner-Fairbank Highway Research Center, FHWA, 6300 Georgetown Pike, McLean, VA 22101, United States. The authors would like to express their acknowledgments to the NRC Research Associateship Program. However, the opinions and conclusions expressed in this paper are solely those of the writers and do not necessarily reflect the views of the sponsors.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data used by this research are publicly available through FHWA InfoBridge™ (https://infobridge.fhwa.dot.gov/). The data and code will be available from the corresponding author upon reasonable request.