Numerical and experimental evaluation of structural changes using sparse auto-encoders and svm applied to dynamic responses

HIGHLIGHTS

  • who: Rafaelle Piazzaroli Finotti and colleagues from the Graduate Program in Computational Modeling, Federal University of Juiz Fora, Juiz Fora , have published the article: Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses, in the Journal: (JOURNAL)
  • what: This work evaluates the deep learning algorithm called (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 (SHM) field especially when dealing with real-case structures. Taking . . .

     

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