Neural Networks-Based Damage Detection for Bridges Using Ambient Vibration Data due to Ordinary Traffic Loadings

摘要

Structural health monitoring has become an important research topic in conjunction with damage assessment and safety evaluation of structures. The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in signal analysis and information processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. This study presents the results of the neural networksbased damage detections for several bridge structures using ambient vibration data. The modal parameters identified from ambient vibration data induced by the ordinary traffics are used as the input to the neural networks. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors in the baseline finite element model than the mode shapes themselves. A numerical example analysis on a simple beam was presented to demonstrate the effectiveness of the proposed method. Results of laboratory test on a simply supported bridge model and field test on a bridge with multiple girders confirm the applicability of the present method.