Prediction of bridge deck condition rating based on artificial neural networks
An accurate prediction of the future condition of structural components is essential for planning the maintenance, repair, and rehabilitation of bridges. As such, this paper presents an application of Artificial Neural Networks (ANN) to predict future deck condition for highway bridges in the State of Alabama, the United States. A library of 2572 bridges was extracted from the National Bridge Inventory (NBI) database and used for training, validation, and testing the ANN model, which had eight input parameters and one output being the deck rating. Specifically, the eight input parameters are Current Bridge Age, Average Daily Traffic, Design Load, Main Structure Design, Approach Span Design, Number of main Span, Percent of Daily Truck Traffic, and Average Daily Traffic Growth Rate. The results indicated the obtained ANN model can predict the condition rating of the bridge deck with an accuracy of 73.6%. If a margin error of ±1 was used, the accuracy of the proposed model reached a much higher value of 98.5%. Besides, a sensitivity analysis was conducted for individual input parameters revealed that Current Bridge Age was the most important predicting parameter of bridge deck rating. It was followed by the Design Load and Main Structure Design. The other input parameters were found to have neglectable effects on the ANN’s performance. Finally, it was shown that the obtained ANN can be used to develop the deterioration curve of the bridge deck, which helps visualize the condition rating of a deck, and accordingly the maintenance need, during its remaining service life.
condition rating; bridge deck; deterioration curve; artificial neural networks; sensitivity analysis.
Copyright (c) 2019 National University of Civil Engineering
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.