Comparison of statistical and non-statistical artificial neural network methods for damage detection using modal data

Abstract

The effectiveness of Artificial Neural Networks (ANNs) when applied to pattern recognition in vibration-based damage detection has been demonstrated in many studies because they are capable of providing accurate results and the reliable identification of structural damage based on modal data. However, the use of ANNs has been questioned in terms of its reliability in the face of uncertainties in measurement and modeling data. Attempts to incorporate a probabilistic method into an ANN by treating the uncertainties as normally distributed random variables has delivered promising solutions to this problem, but the probabilistic method is less straightforward in practice because it is often not possible to obtain unbiased probabilistic distributions of the uncertainties. As an alternative, a non-statistical method has been proposed in many studies to address the problem of uncertainty in vibration damage detection. Compared to the conventional probabilistic analysis, non-probabilistic interval analysis does not require any assumption about the uncertainties’ distribution. It requires only the upper and lower bounds of the uncertain parameters, which can generally be obtained in real engineering problems. Thus, damage detection with noisy data becomes simpler and computationally less complex. This study compares both methods using ANN for damage detection. A numerical steel frame is used as an example. The results show that the non-statistical method provides more reliable results compared to the conventional statistical method in detecting damage with noisy data.