An integrated approach for structural damage identification using wavelet neuro-fuzzy model

·The structural response signal processing method identifies damage time and location.·Wavelet real-time filtering algorithm processes response signals with noise up to 30%.·ANFIS is trained with structural input–output signal data and used to model structural behavior.·Interval modeling technique is used to quantify structural system uncertainty with dominant uncertainty coordinate.·The damage identification accuracy is up to 0.05% under 10% noise.

Structural damage can be identified by processing structural vibration response signals and excitation data, and thus the suitability of signal processing methods is essential to structural damage identification. To explore an intelligent signal processing method for structural damage identification, the paper integrated wavelet real-time filtering algorithm, Adaptive Neruo-Fuzzy Inference System (ANFIS) and interval modeling technique to process structural response signals and excitation data. With Wavelet Transform (WT) algorithm filtering random noise, ANFIS was found to model the structural behavior properly and interval modeling technique to quantify damage index accurately. The rapid identifications of several unknown damages and small damages indicate the efficiency of this integrated method. The comparison of these results and some other signal processing methods shows that, the proposed method can be used to identify both the time and the location when the structural damage occurs unexpectedly.