Damage detection using artificial neural network with consideration of uncertainties

Artificial Neural Networks (ANN) have received increasing attention for use in detecting damage in structures based on vibration modal parameters. However, uncertainties existing in the finite element model used and the measured vibration data may lead to false or unreliable output result from such networks. In this study, a statistical approach is proposed to take into account the effect of uncertainties in developing an ANN model. By applying Rosenblueth’s point estimate method verified by Monte Carlo simulation, the statistics of the stiffness parameters are estimated. The probability of damage existence (PDE) is then calculated based on the probability density function of the existence of undamaged and damaged states. The developed approach is applied to detect simulated damage in a numerical steel portal frame model and also in a laboratory tested concrete slab. The effects of using different severity levels and noise levels on the damage detection results are discussed.