A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Neural networks-based damage detection for bridges considering errors in baseline finite element models
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. In this study, a neural networks-based damage detection method using the modal properties is presented, which can effectively consider the modelling errors in the baseline finite element model from which the training patterns are to be generated. 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 modelling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are 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.
Neural networks-based damage detection for bridges considering errors in baseline finite element models
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. In this study, a neural networks-based damage detection method using the modal properties is presented, which can effectively consider the modelling errors in the baseline finite element model from which the training patterns are to be generated. 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 modelling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are 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.
Neural networks-based damage detection for bridges considering errors in baseline finite element models
Auf neuralen Netzwerken basierende Schadenserkennung bei Brücken unter Berücksichtigung von diskretisierungsabhängigen Fehlern bei Finite-Elemente-Modellen
Lee, Jong Jae (author) / Lee, Jong Won (author) / Yi, Jin Hak (author) / Yun, Chung Bang (author) / Jung Hie Young (author)
Journal of Sound and Vibration ; 280 ; 555-578
2005
24 Seiten, 19 Bilder, 8 Tabellen, 39 Quellen
Article (Journal)
English
Mechanik , Schadenfrüherkennung , Bewertung , Systemidentifikation , neuronales Netzwerk , genetischer Algorithmus , Modalanalyse , mathematisches Modell , numerische Simulation , Finite-Elemente-Methode , experimentelle Untersuchung , Brückenbau , Bauingenieurwesen , Modellversuch , Lokalisierung , Strukturdynamik
Damage Detection for Underground Arch Structure Considering Errors in Baseline Model
| Trans Tech Publications | 2010
Damage Detection for Underground Arch Structure Considering Errors in Baseline Model
| British Library Conference Proceedings | 2011
Neural Networks for Damage Detection in Steel Railway Bridges
| British Library Conference Proceedings | 1995
Probabilistic Baseline of Finite Element Model of Bridges under Environmental Temperature Changes
| Online Contents | 2017
Damage localization for bridges using probabilistic neural networks
| Springer Verlag | 2007