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

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.

[1]  G C Lee,et al.  NEURAL NETWORKS TRAINED BY ANALYTICALLY SIMULATED DAMAGE STATES , 1993 .

[2]  C. Peng,et al.  Analysis of complex time series using refined composite multiscale entropy , 2014 .

[3]  Ian Flood,et al.  Neural Networks in Civil Engineering. I: Principles and Understanding , 1994 .

[4]  Pavan Kumar Kankar,et al.  Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier , 2015 .

[5]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Hoon Sohn,et al.  Structural Health Monitoring Using Statistical Process Control , 2000 .

[7]  Chung-Kang Peng,et al.  Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations , 2015, Entropy.

[8]  Lambros S. Katafygiotis,et al.  Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study , 2004 .

[9]  Ning Xinbao Multiscale entropy analysis of complex physiologic time series , 2007 .

[10]  Jian-Jiun Ding,et al.  Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine , 2012, Entropy.

[11]  Tzu-Kang Lin,et al.  Performance Evaluation of an Entropy-Based Structural Health Monitoring System Utilizing Composite Multiscale Cross-Sample Entropy , 2019, Entropy.

[12]  James H. Garrett,et al.  Use of neural networks in detection of structural damage , 1992 .

[13]  Chun-Chieh Wang,et al.  Time Series Analysis Using Composite Multiscale Entropy , 2013, Entropy.