An Ensemble Neural Network for Damage Identification in Steel Girder Bridge Structure Using Vibration Data

Sjs Hakim,Zm Jaini,Af Kamarudin,S Shahidan,Sn Mokhatar,Nh Abd Ghafar,Tnt Chik

doi:10.13189/cea.2021.090225

Abstract

Damage detection has the ability to prevent the occurrence of unpredictable failures and increase the serviceability of structures. Vibration-based damage detection methods are due to the fact that the damages will change the dynamic characteristics of a structure, such as natural frequencies, mode shapes and damping ratios. Resultantly, structural capacity is usually impacted, which subsequently, adversely affects performance. Fortunately, artificial neural networks (ANNs) have emerged as one of the most powerful learning tools, inspired by biological nervous systems. Unsurprisingly, the said technique has been applied for structural damage identification in the past decades. Relatedly, the objective of this study was to investigate the potential of ensemble neural network-based damage detection techniques in a scaled steel girder bridge structure using dynamic parameters. Experimental and finite element analyses of the structure were performed to generate modal parameters and study the efficiency of the ensemble neural networks in order to improve structural damage identification. Pursuant to the damage identification results, the ensemble ANN-based damage identification method was able to detect and locate damage with a high level of accuracy.

Highlights

It is general knowledge that severe damage to structures can deteriorate their structural stability and lead to major disasters, and as such, it is necessary to detect the location and size of any damage as early as possible
Structural damage detection is the identification of damage presence and its location as well as the extent of damage imposed to the structure
Vibration-based structural health monitoring (SHM) methods have been widely studied as a promising alternative for effective structural damage detection

Summary

Introduction

It is general knowledge that severe damage to structures can deteriorate their structural stability and lead to major disasters, and as such, it is necessary to detect the location and size of any damage as early as possible. Detection and monitor of structural damage can help to maintain the structure in due time, and this is extremely important to prevent sudden and catastrophic collapse, preserving the service life of civil structures. Structural health monitoring (SHM) methods have been applied to evaluate the condition of structures, and one of the most important aspects in SHM is damage detection. Vibration-based SHM methods have been widely studied as a promising alternative for effective structural damage detection. Damage can be detected by observing these structure characteristics [1,2,3,4]

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