The results indicated the obtained ANN model can predict the condition rating of the bridge deck with an accuracy of . If a margin error of ±1 was used, the accuracy of the proposed model reached a much higher value of . 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. | Prediction of bridge deck condition rating based on artificial neural networks
