Frequency Response Function-based Structural Damage Identification using Artificial Neural Networks-a Review

S.J.S Hakim,H Abdul Razak

doi:10.19026/rjaset.7.459

Abstract

This study presents and reviews the technical literature and previous studies for the past three decades on structural damage identification using ANNs and measured FRFs as inputs. Much of the previous studies have used modal parameters to ascertain the success of damage identification. However, significant information may not be properly represented through the application of modal parameters. With this in mind, the direct use of frequency domain data in terms of the Frequency Response Functions (FRFs) seems more appropriate. Recent studies indicate that ANNs can be trained on measured FRFs of healthy and damaged models of structure to assess the condition of the structure. According to this review, it is clear that there have been numerous studies which have gone on to apply the ANNs on FRF data of structures in the field of damage identification and it has been shown that ANNs using FRFs can provide several advantages over the modal parameters and damage identification has subsequently become much improved.

Highlights

The existing civil structures are prone to various damages and degrade during their service life
During the last three decades, a lot of studies using various methods in the area of damage assessment have been conducted and reviewed, but to date, there is no review regarding the application of Artificial Neural Networks (ANNs) for structural damage detection using the frequency domain data such as Frequency Response Functions (FRFs)
The FRF data are used in the training of neural network and the network output determines whether the structure is damaged or not, based on experimental measurements

Summary

Introduction

The existing civil structures are prone to various damages and degrade during their service life. Frequency domain data in terms of FRFs have been applied by several researchers to the training of ANNs for the purpose of structural damage detection, which will be explained .

Results

Conclusion