An ensemble neural network for damage identification in steel girder bridge structure using vibration data

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.