Abstract
The aim of this study was to develop an entropy-based structural health monitoring system for solving the problem of unstable entropy values observed when multiscale cross-sample entropy (MSCE) is employed to assess damage in real structures. Composite MSCE was utilized to enhance the reliability of entropy values on every scale. Additionally, the first mode of a structure was extracted using ensemble empirical mode decomposition to conduct entropy analysis and evaluate the accuracy of damage assessment. A seven-story model was created to validate the efficiency of the proposed method and the damage index. Subsequently, an experiment was conducted on a seven-story steel benchmark structure including 15 damaged cases to compare the numerical and experimental models. A confusion matrix was applied to classify the results and evaluate the performance over three indices: accuracy, precision, and recall. The results revealed the feasibility of the modified structural health monitoring system and demonstrated its potential in the field of long-term monitoring.
Highlights
Over the preceding few decades, structural health monitoring (SHM) techniques have been developed for early damage detection in various engineering fields
Case,the thevelocity velocitysignals signalsofoftwo twovertically verticallyadjacent adjacentfloors floorswere wereprocessed processed after the coarse-graining procedure to evaluate the degree of dissimilarity between through composite multiscale cross-sample entropy (CMSCE) after the coarse-graining procedure to evaluate the degree of dissimilarity between floors
The CMSCE method was utilized to enhance the reliability of entropy
Summary
Over the preceding few decades, structural health monitoring (SHM) techniques have been developed for early damage detection in various engineering fields. Novel SHM methods based on signal processing techniques have been proposed for analyzing measured responses in succession. Dynamic monitoring entails measuring the displacement, velocity or acceleration signals of structures to obtain time–frequency characteristics. In 2003, Chang [3] summarized the limitations and applications of vibration-based SHM methods. The impact of measuring noise, environmental and damage on the sensitivity of the damage detection was analyzed [4]. In 2016, Amezquita-Sanchez et al summarized the current signal processing techniques for vibration-based SHM and point out its advantages and disadvantages [5]. Opoka et al applied the root mean square deviation (RMSD)
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