A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Damage detection in bridges using neural networks for pattern recognition of vibration signatures
AbstractDamage 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.
Damage detection in bridges using neural networks for pattern recognition of vibration signatures
AbstractDamage 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.
Damage detection in bridges using neural networks for pattern recognition of vibration signatures
Yeung, W.T. (author) / Smith, J.W. (author)
Engineering Structures ; 27 ; 685-698
2004-12-17
14 pages
Article (Journal)
Electronic Resource
English
Neural Networks for Damage Detection in Steel Railway Bridges
| British Library Conference Proceedings | 1995
Damage localization for bridges using probabilistic neural networks
| Springer Verlag | 2007
| British Library Online Contents | 2017
Pattern Recognition of Acoustic Signatures Using ART2-A Neural Network
| British Library Online Contents | 1996