Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review

Abstract

Structural health monitoring (SHM) methods are essential to guarantee the safety and integrity of composite structures, which are extensively utilized in aerospace, automobile, marine, and infrastructure industry. The deterioration of composite structures is primarily caused by operational and environmental variability. To address this issue, artificial intelligence (AI) techniques are being integrated into the SHM systems to enhance the performance of composite structures via digital transformation and big data analysis. Therefore, the present article aims to provide a critical review of AI models, including machine learning, deep learning, and transfer learning, to preserve and sustain composite structures throughout their life. The article covers the complete SHM process for composite structures, including sensing technologies, data-preprocessing, feature extraction, and decision-making process. Thus, the health monitoring of composites is presented in consideration of modern AI techniques, accompanied by the identification of current challenges and potential future research directions.

Keywords:

• artificial intelligence
• structural health monitoring
• composite structures
• machine learning
• deep learning
• transfer learning
• damage detection

Acknowledgements

This work was supported by the Dongguk University Research Fund of 2023, and by a National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (No. 2020R1A2C1006613).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.