Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies

Abstract

The main problem in damage assessment is the determination of how to ascertain the presence, location, and severity of structural damage given the structure’s dynamic characteristics. The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms. However, these algorithms run quite slowly, and the damage assessment process is achieved via a costly and time-consuming inverse process, which presents an obstacle for real-time health monitoring applications. Artificial neural networks (ANN) have recently been introduced as an alternative to model updating methods. Once a neural network has been properly trained, it can potentially detect, locate, and quantify structural damage in a short period of time and can therefore be applied for real-time damage assessment. The primary contribution of this research is the development of a real-time damage assessment algorithm using ANN and antiresonant frequencies. Antiresonant frequencies can be identified more easily and more accurately than mode shapes, and they provide the same information.This research addresses the setup of the neural network parameters and provides guidelines for the selection of these parameters in similar damage assessment problems. Two experimental cases validate this approach: an 8-DOF mass-spring system and a beam with multiple damage scenarios.