Abstract
Acoustic emission (AE) and vibration signal are significant criteria of damage identification in structural health monitoring (SHM) engineering. Multi-disciplinary knowledge and synergistic parameter effects are technical challenges for damage assessment modelling. This study proposes a structural damage cause-and-effect analysis method based on parameter information entropy. Monitoring data is used to form a time-domain feature wave (TFW). The structural strength degradation factor (DF) would be used to define structural damage information entropy (SDIE) vector. The structural damage cause and effect model is developed in a probability sense. A fatigue index is adopted for damage assessment, and a causal strength index is proposed to locate the most likely damage cause. A sandstone-truss structure experiment was conducted to show that the proposed method is effective for damage evaluation and the experimental results provide strong support. This is a statistical damage identification method based on causal logic uncertainty, meaning a complicated mechanics calculation can be avoided.
Highlights
Structural health monitoring (SHM) is significant to ensure engineering system safety
For the sake of reciprocal analysis between damage events and EF with multi-parameter integrated influence, a structural damage cause-and-effect analysis model based on parameter information entropy is proposed in this study
Let the contribution weight k be 1 since the above four Acoustic emission (AE) parameters have no significant difference in sandstone moisture damage assessment
Summary
Structural health monitoring (SHM) is significant to ensure engineering system safety. For the sake of reciprocal analysis between damage events and EF with multi-parameter integrated influence, a structural damage cause-and-effect analysis model based on parameter information entropy is proposed in this study. This model quantifies the damage occurrence from the perspective of probability uncertainty. Both damage level assessment and identification of the most likely cause and location can be obtained This method combines the statistical causal analysis and the basic engineering parameters. Compared with traditional damage signal processing methods (such as Moment tensor, Artificial neural networks), the uncertainty information of multiple damage factors can be considered synergistically Complex calculations such as constitutive equations, vibration characteristics analysis, multi-physics coupling effect, etc., are not required
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