Self-supervised structural damage detection method based on pretext tasks learning feature representations

Wenhao He; Kaizhi Chen; Shangping Zhong

doi:10.1117/12.3009391

16 October 2023 Self-supervised structural damage detection method based on pretext tasks learning feature representations

Wenhao He, Kaizhi Chen, Shangping Zhong

Author Affiliations +

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128033A (2023) https://doi.org/10.1117/12.3009391
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Machine learning has become an influential and effective tool in numerous civil engineering applications, especially in the field of structural health monitoring (SHM). Recently, the emergence of self-supervised learning has led to the development of many industries, and its accuracy and stability are superior to previous methods. Self-supervised learning learns generalizable information representation from unlabeled mixed data by solving pretext tasks, and this feature is exactly in line with the mixed and unlabeled data in the SHM field. This is of great significance to the improvement of detection accuracy in SHM practical applications. Therefore, this paper proposes a new self-supervised method for structural damage detection. The key to this method is that we use two self-supervised pretext tasks to learn the latent feature representation of the data, and we introduce homoscedastic uncertainty for automatically assigning weights to the two pretext tasks. The relative confidence between tasks is captured, the impact of noise on tasks is reduced, the pretext tasks can better learn data feature representation, and the purpose of improving the accuracy of damage detection is achieved.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Wenhao He, Kaizhi Chen, and Shangping Zhong "Self-supervised structural damage detection method based on pretext tasks learning feature representations", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128033A (16 October 2023); https://doi.org/10.1117/12.3009391

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KEYWORDS

Machine learning

Data modeling

Damage detection

Education and training

Structural health monitoring

Performance modeling

Ablation

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