Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring...

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Bibliographic Details
Main Authors:	Rafaelle Piazzaroli Finotti, Flávio de Souza Barbosa, Alexandre Abrahão Cury, Roberto Leal Pimentel
Format:	Article
Language:	English
Published:	MDPI AG 2021-12-01
Series:	Applied Sciences
Subjects:	novelty detection vibration signals Sparse Auto-Encoder damage detection structural health monitoring
Online Access:	https://www.mdpi.com/2076-3417/11/24/11965

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