A Hybrid Learning Framework for Enhancing Bridge Damage Prediction

Bridges are crucial structures for transportation networks, and their structural integrity is paramount. Deterioration and damage to bridges can lead to significant economic losses, traffic disruptions, and, in severe cases, loss of life. Traditional methods of bridge damage detection, often relying on visual inspections, can be challenging or impossible in critical areas such as roofing, corners, and heights. Therefore, there is a pressing need for automated and accurate techniques for bridge damage detection. In this paper, we propose a novel method for bridge damage detection that leverages a hybrid supervised and unsupervised learning strategy. Our approach combines pixel-based feature method LBP with the mid-level feature BoVW for feature extraction, followed by Apriori algorithm for dimensionality reduction and optimal feature selection. The selected features are then trained using the MobileNet model. The proposed model demonstrates exceptional performance, achieving accuracy rates ranging from 98.27% to 100% with error rates between 1.73% and 0% across multiple bridge damage datasets. This study contributes a reliable hybrid learning framework for minimizing error rates in bridge damage detection, showcasing the potential of combining LBP-BoVW features with MobileNet for image-based classification tasks.