Continual-learning-based framework for structural damage recognition
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913483482726400 |
|---|---|
| author | Shu, Jiangpeng Zhang, Jiawei Ly, Reachsak Lin, Fangzheng Duan, Yuanfeng |
| author_facet | Shu, Jiangpeng Zhang, Jiawei Ly, Reachsak Lin, Fangzheng Duan, Yuanfeng |
| contents | Multi-damage is common in reinforced concrete structures and leads to the requirement of large number of neural networks, parameters and data storage, if convolutional neural network (CNN) is used for damage recognition. In addition, conventional CNN experiences catastrophic forgetting and training inefficiency as the number of tasks increases during continual learning, leading to large accuracy decrease of previous learned tasks. To address these problems, this study proposes a continuallearning-based damage recognition model (CLDRM) which integrates the learning without forgetting continual learning method into the ResNet-34 architecture for the recognition of damages in RC structures as well as relevant structural components. Three experiments for four recognition tasks were designed to validate the feasibility and effectiveness of the CLDRM framework. In this way, it reduces both the prediction time and data storage by about 75% in four tasks of continuous learning. Three experiments for four recognition tasks were designed to validate the feasibility and effectiveness of the CLDRM framework. By gradual feature fusion, CLDRM outperformed other methods by managed to achieve high accuracy in the damage recognition and classification. As the number of recognition tasks increased, CLDRM also experienced smaller decrease of the previous learned tasks. Results indicate that the CLDRM framework successfully performs damage recognition and classification with reasonable accuracy and effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Continual-learning-based framework for structural damage recognition Shu, Jiangpeng Zhang, Jiawei Ly, Reachsak Lin, Fangzheng Duan, Yuanfeng Computer Vision and Pattern Recognition Artificial Intelligence Multi-damage is common in reinforced concrete structures and leads to the requirement of large number of neural networks, parameters and data storage, if convolutional neural network (CNN) is used for damage recognition. In addition, conventional CNN experiences catastrophic forgetting and training inefficiency as the number of tasks increases during continual learning, leading to large accuracy decrease of previous learned tasks. To address these problems, this study proposes a continuallearning-based damage recognition model (CLDRM) which integrates the learning without forgetting continual learning method into the ResNet-34 architecture for the recognition of damages in RC structures as well as relevant structural components. Three experiments for four recognition tasks were designed to validate the feasibility and effectiveness of the CLDRM framework. In this way, it reduces both the prediction time and data storage by about 75% in four tasks of continuous learning. Three experiments for four recognition tasks were designed to validate the feasibility and effectiveness of the CLDRM framework. By gradual feature fusion, CLDRM outperformed other methods by managed to achieve high accuracy in the damage recognition and classification. As the number of recognition tasks increased, CLDRM also experienced smaller decrease of the previous learned tasks. Results indicate that the CLDRM framework successfully performs damage recognition and classification with reasonable accuracy and effectiveness. |
| title | Continual-learning-based framework for structural damage recognition |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2408.15513 |