Damage detection in bridges using neural networks for pattern recognition of vibration signatures

Abstract

Damage detection by measurement of vibration signatures is highly attractive for monitoring bridges because it provides the possibility of electronic recording combined with digital processing and report generation. However, changes in frequency and mode shape caused by damage are usually small and methods that have demonstrated successful detection have generally been confined to small-scale laboratory models. In this investigation a damage detection procedure, using pattern recognition of the vibration signature, was assessed using a finite element model of a real structure — a suspension bridge more than 100 years old. Realistic damage scenarios were simulated and the response under moving traffic was evaluated. Feature vectors generated from the response spectra were presented to two unsupervised neural networks for examination. It is shown that the sensitivity of the neural networks may be adjusted so that a satisfactory rate of damage detection may be achieved even in the presence of noisy signals.