Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation

A fuzzy logic system (FLS) with a new sliding window defuzzifier is developed for damage detection. The effect of changes in the damage evaluation parameter (frequency) due to uncertainty in material properties is explored and the results of the probabilistic analysis are used to develop a robust FLS for damage detection. Probabilistic analysis is performed using Monte Carlo Simulation (MCS) on a beam finite element (FE) model to calculate statistical properties of the variation in natural frequencies of the beam due to structural damage and material uncertainty. Variation in these frequency measures, further contaminated with measurement noise, are used for testing the FLS. The FLS developed for damage detection in the steel beam having material uncertainty (elastic modulus) with coefficient of variation (COV) of 3 percent and noise level of 0.15 in the measurement data, correctly identifies the fault with an accuracy of about 94 percent. The FLS also accurately classifies the undamaged condition in presence of the mentioned uncertainties reducing the possibility of false alarms. From an algorithmic standpoint, this paper connects the disparate areas of probability and fuzzy logic to alleviate uncertainty issues in damage detection.