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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied System Innovation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-5577/8/3/61 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. This study aims to propose a novel method for bridge crack detection that leverages a hybrid supervised and unsupervised learning strategy. The proposed approach combines pixel-based feature method local binary pattern (LBP) with the mid-level feature bag of visual words (BoVW) for feature extraction, followed by the 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. |
|---|---|
| ISSN: | 2571-5577 |