Substructuring Technique for Damage Detection Using Statistical Multi-Stage Artificial Neural Network

Abstract

Artificial Neural networks (ANN) have been proven in many studies to be able to efficiently detect damage from vibration measurements. Their capability to recognize patterns and to handle non-linear and non-unique problems provides an advantage over traditional mathematical methods in correlating the vibration data to damage location and severity. However, one shortcoming of ANN is they require enormous computational effort and sometimes prohibitive time and computer memory for training a reliable ANN model, especially when structures with many degrees of freedom are involved. Therefore, in most cases, rather large elements are used in the structure model to reduce the degrees of freedom. This results in the structural vibration properties not being sensitive to small damage in a large element. As a result, direct application of ANN to detecting damage in a large civil engineering structures is not feasible. In this study, a multi-stage ANN incorporating a probability method is proposed to tackle this problem. Through this method, a structure is divided into several substructures, and each substructure is assessed independently. In each subsequent stage, only the damaged substructures are analyzed, and eventually the location and severity of small structural damage can be detected. This approach greatly reduces the computational time and the required computer memory. Moreover, a probabilistic method is also used to include the uncertainties in vibration frequencies and mode shapes in damage detection analysis. It is found that this method reduces the uncertainty effect in frequencies due to duplication error in the multi-stage ANN model and reduces the uncertainty effect in mode shapes due to the damage in other substructures. The developed approach is applied to detect damage in numerically simulated and laboratory tested concrete slab. The results demonstrate that the proposed method can detect small damage with a higher level of confidence, and the undamaged elements are less likely to be falsely detected.