標題: Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring
作者: Lin, Tzu-Kang
Chen, Yu-Ching
土木工程學系
Department of Civil Engineering
關鍵字: structural health monitoring;artificial neural network;multi-scale cross-sample entropy
公開日期: 1-Feb-2020
摘要: 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.
URI: http://dx.doi.org/10.3390/app10030839
http://hdl.handle.net/11536/154378
DOI: 10.3390/app10030839
期刊: APPLIED SCIENCES-BASEL
Volume: 10
Issue: 3
起始頁: 0
結束頁: 0
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