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.
Highlights
Disasters such as earthquakes and typhoons degrade the strength of structures
70% simulated a healthy condition, and the case involving a residual stiffness of 35% simulated a a healthy condition, and the case involving a residual stiffness of 35% simulated a damage condition
This study proposes an structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and backpropagation neural network (BPN) and conducted numerical simulations to assess the performance of the system
Summary
Disasters such as earthquakes and typhoons degrade the strength of structures. To protect property and lives, the structural health of buildings must be inspected after such natural disasters.In civil engineering, structural health monitoring (SHM) is applied to detect damage to structures.Novel SHM methods based on signal processing techniques have recently been proposed to analyze measured structural responses. To protect property and lives, the structural health of buildings must be inspected after such natural disasters. Structural health monitoring (SHM) is applied to detect damage to structures. Novel SHM methods based on signal processing techniques have recently been proposed to analyze measured structural responses. In 2001, Sohn et al [1] applied a statistical process control technique and a pattern recognition technique to diagnose the vibration-based damage. Lam et al [2] utilized the model updating approach to set up a damage criterion in 2004. Structural dynamic responses, such as velocity and acceleration, can be collected to determine the structural health condition of a building
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