Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring

Tzu Kang Lin*, Yu Ching Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.

Original languageEnglish
Article number839
JournalApplied Sciences (Switzerland)
Volume10
Issue number3
DOIs
StatePublished - 1 Feb 2020

Keywords

  • Artificial neural network
  • Multi-scale cross-sample entropy
  • Structural health monitoring

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