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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/8/3/61 |
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| author | Amal Abdulbaqi Maryoosh Saeid Pashazadeh Pedram Salehpour |
| author_facet | Amal Abdulbaqi Maryoosh Saeid Pashazadeh Pedram Salehpour |
| author_sort | Amal Abdulbaqi Maryoosh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-69d66d25cf56476080f3f5acfe2a5daf |
| institution | Kabale University |
| issn | 2571-5577 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj-art-69d66d25cf56476080f3f5acfe2a5daf2025-08-20T03:27:15ZengMDPI AGApplied System Innovation2571-55772025-04-01836110.3390/asi8030061A Hybrid Learning Framework for Enhancing Bridge Damage PredictionAmal Abdulbaqi Maryoosh0Saeid Pashazadeh1Pedram Salehpour2Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, IranDepartment of Information Technology, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, IranDepartment of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, IranBridges 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.https://www.mdpi.com/2571-5577/8/3/61association rule miningBoVWbridge crackdeep learninglocal binary pattern |
| spellingShingle | Amal Abdulbaqi Maryoosh Saeid Pashazadeh Pedram Salehpour A Hybrid Learning Framework for Enhancing Bridge Damage Prediction Applied System Innovation association rule mining BoVW bridge crack deep learning local binary pattern |
| title | A Hybrid Learning Framework for Enhancing Bridge Damage Prediction |
| title_full | A Hybrid Learning Framework for Enhancing Bridge Damage Prediction |
| title_fullStr | A Hybrid Learning Framework for Enhancing Bridge Damage Prediction |
| title_full_unstemmed | A Hybrid Learning Framework for Enhancing Bridge Damage Prediction |
| title_short | A Hybrid Learning Framework for Enhancing Bridge Damage Prediction |
| title_sort | hybrid learning framework for enhancing bridge damage prediction |
| topic | association rule mining BoVW bridge crack deep learning local binary pattern |
| url | https://www.mdpi.com/2571-5577/8/3/61 |
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