Damage assessment of a beam using Artificial Neural Networks and antiresonant frequencies

Abstract

The main problem of damage assessment is 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 using global optimization algorithms. Nevertheless, these algorithms are very slow, and the damage assessment process is achieved through a costly and time-consuming inverse process. This is a problem for real-time health monitoring applications. Artificial Neural Networks (ANN) have been recently 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. Hence, it can be used 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 easier and more accurately than mode shapes and still provide the same information. An experimental beam with multiple damage scenarios is used to validate the approach. © The Society for Experimental Mechanics, Inc. 2013.