Abstract:Structural damage identification is always a hot topic in structural health monitoring and safe state evaluation. Here, a two-step method for structural damage identification based on Mahalanobis distance accumulation (MDC) and empirical mode decomposition (EMD) was proposed. Firstly, health state monitoring data were taken as the reference sample, and its MDC was used to construct the damage identification vector. This vector’s MDC average value was taken as the threshold to do preliminary damage identification for samples to be tested. When damage identification using directly monitored data was difficult due to less damage information and lower signal-to-noise ratio, the EMD method was used to decompose the monitored data into various intrinsic mode functions (IMFs). Then, MDC values of various IMFs were used to construct damage identification vectors, and the statistical method was used to do probability density function fitting for damage identification vectors. The upper limit of the probability density function within its 95% confidence interval was taken as the threshold to do further structural damage identification. Numerical simulation for a simply supported beam and model tests of I-steel verified the effectiveness and anti-noise of the proposed method.
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