Improved SOM algorithm for damage characterization based on visual sensing

• Autorzy: Zhu Hongtao , Guo Shuyun

Identyfikatory

DOI

10.24425/ace.2025.154112

• Języki publikacji: EN

Abstrakty

EN

In the field of concrete structure health monitoring, accurately and swiftly identifying damage characteristics stands as a pivotal task. To enhance the accuracy and efficiency of concrete damage identification, this research proposes an improved Self-Organizing Map algorithm based on visual sensing. By optimizing feature extraction and representation methods, introducing novel learning strategies, and incorporating spatial attention mechanisms, the model becomes adept at capturing and identifying concrete damage features more effectively. Additionally, employing stochastic gradient descent as an optimization algorithm enhances the model training efficiency. Experimental results showcase that the model exhibits a detection time of merely 0.8 seconds, while demonstrating outstanding fitting and clustering performance, achieving an actual accuracy of 98.2%. Compared to methods based on digital image monitoring and deep learning detection, it shows an improvement of 12.7% and 31.8%, respectively. The proposed enhanced model significantly augments the accuracy and efficiency of concrete damage identification, providing an effective solution for the health monitoring of concrete structures, particularly in scenarios requiring large-scale and real-time monitoring. This advancement elevates the practicality and convenience of concrete damage detection, propelling progress in the field of building safety.

Słowa kluczowe

EN

computer vision; damage detection; self-organizing map; map algorithm; attention mechanism

PL

widzenie komputerowe; wykrywanie uszkodzeń; mapa samoorganizująca; algorytm mapy; mechanizm uwagi

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2025
• Tom: Vol. 71, nr 2
• Strony: 129--142
• Opis fizyczny: Bibliogr. 20 poz., il., tab.

Twórcy

autor

Zhu Hongtao

• School of Civil Engineering, Xinyang College, Xinyang, China

• https://orcid.org/0009-0001-8511-9005

autor

Guo Shuyun

• School of Foreign Languages, Gushi Vocational Education Center, Xinyang, China

• https://orcid.org/0009-0008-1065-3300

Bibliografia

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