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005			20250625162445.0
040			_cCICY
090			_aB-20261
245	1	0	_aIntelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review
490	0		_vAdvanced Composite Materials, 33(2), p.162-188, 2023
520	3		_aStructural 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.
650	1	4	_aARTIFICIAL INTELLIGENCE
650	1	4	_aSTRUCTURAL HEALTH MONITORING
650	1	4	_aCOMPOSITE STRUCTURES
650	1	4	_aMACHINE LEARNING
650	1	4	_aDEEP LEARNING
650	1	4	_aTRANSFER LEARNING
650	1	4	_aDAMAGE DETECTION
700	1	2	_aAzad, M. M.
700	1	2	_aKim, S.
700	1	2	_aCheon, Y. B.
700	1	2	_aKim, H. S.
856	4	0	_uhttps://drive.google.com/file/d/1vNL32cmucHjJJ799KqWp-utL6x-d_xFF/view?usp=drivesdk _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx
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