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A platform for research: civil engineering, architecture and urbanism
Application of Deep Convolutional Neural Network for Assessing Fracture Risks of Coastal Construction
https://orcid.org/http://orcid.org/0000-0001-7581-2212
https://orcid.org/http://orcid.org/0000-0002-5367-3992
https://orcid.org/http://orcid.org/0000-0001-7253-4536
https://orcid.org/http://orcid.org/0009-0009-4037-182X
https://orcid.org/http://orcid.org/0009-0006-8718-0877
Coastal constructions (CCs) operate in harsh environmental conditions under many external forces. Therefore, the coastal construction damaged detectors based on the evaluation of cracks’ progression play a crucial role in ensuring the safety of people and facilities. The proposed method analyzes and monitors the sequence of consecutive images collected by a camera system mounted at locations where cracks occur in the critical structure. A deep convolutional neural network (DCNN) is applied to identify and analyze crack evolution over time. By using parameters such as crack width, boundary, and progression, the proposed system assesses the crack progression of the constructions over time, thereby improving the level of safety and guaranteeing stable operation of the CCs in working conditions.
Application of Deep Convolutional Neural Network for Assessing Fracture Risks of Coastal Construction
https://orcid.org/http://orcid.org/0000-0001-7581-2212
https://orcid.org/http://orcid.org/0000-0002-5367-3992
https://orcid.org/http://orcid.org/0000-0001-7253-4536
https://orcid.org/http://orcid.org/0009-0009-4037-182X
https://orcid.org/http://orcid.org/0009-0006-8718-0877
Coastal constructions (CCs) operate in harsh environmental conditions under many external forces. Therefore, the coastal construction damaged detectors based on the evaluation of cracks’ progression play a crucial role in ensuring the safety of people and facilities. The proposed method analyzes and monitors the sequence of consecutive images collected by a camera system mounted at locations where cracks occur in the critical structure. A deep convolutional neural network (DCNN) is applied to identify and analyze crack evolution over time. By using parameters such as crack width, boundary, and progression, the proposed system assesses the crack progression of the constructions over time, thereby improving the level of safety and guaranteeing stable operation of the CCs in working conditions.
Application of Deep Convolutional Neural Network for Assessing Fracture Risks of Coastal Construction
https://orcid.org/http://orcid.org/0000-0001-7581-2212
https://orcid.org/http://orcid.org/0000-0002-5367-3992
https://orcid.org/http://orcid.org/0000-0001-7253-4536
https://orcid.org/http://orcid.org/0009-0009-4037-182X
https://orcid.org/http://orcid.org/0009-0006-8718-0877
Coastal constructions (CCs) operate in harsh environmental conditions under many external forces. Therefore, the coastal construction damaged detectors based on the evaluation of cracks’ progression play a crucial role in ensuring the safety of people and facilities. The proposed method analyzes and monitors the sequence of consecutive images collected by a camera system mounted at locations where cracks occur in the critical structure. A deep convolutional neural network (DCNN) is applied to identify and analyze crack evolution over time. By using parameters such as crack width, boundary, and progression, the proposed system assesses the crack progression of the constructions over time, thereby improving the level of safety and guaranteeing stable operation of the CCs in working conditions.
Application of Deep Convolutional Neural Network for Assessing Fracture Risks of Coastal Construction
Lecture Notes in Civil Engineering
Jeng, Dong-Sheng (editor) / Cai, Baoping (editor) / Do, Viet-Dung (author) / Dang, Xuan-Kien (author) / Tran, Tien-Dat (author) / Ly, Soi (author) / Nhu, Khai-Hoan (author)
International conference on coastal and Ocean Engineering ; 2024 ; Shandong, China
Proceedings of 11th International Conference on Coastal and Ocean Engineering ; Chapter: 5 ; 42-53
2025-02-28
12 pages
Article/Chapter (Book)
Electronic Resource
English
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