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Prediction of aeroelastic response of bridge decks using artificial neural networks

  • The assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developedThe assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developed using artificial neural network (ANN). The ANN is implemented in the classical formulation and trained with a comprehensive dataset which is obtained from computational fluid dynamics forced vibration simulations. The input to the ANN is the response time histories of a bridge section, whereas the output is the motion-induced forces. The developed ANN has been tested for training and test data of different cross section geometries which provide promising predictions. The prediction is also performed for an ambient response input with multiple frequencies. Moreover, the trained ANN for aerodynamic forcing is coupled with the structural model to perform fully-coupled fluid--structure interaction analysis to determine the aeroelastic instability limit. The sensitivity of the ANN parameters to the model prediction quality and the efficiency has also been highlighted. The proposed methodology has wide application in the analysis and design of long-span bridges.show moreshow less

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Metadaten
Document Type:Preprint
Author:Dr. Tajammal AbbasGND, Dr. Igor Kavrakov, Prof. Dr. Guido MorgenthalGND, Prof. Dr. Tom LahmerGND
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.4097Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200225-40974Cite-Link
Language:English
Date of Publication (online):2020/01/27
Year of first Publication:2020
Release Date:2020/02/25
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Tag:Aerodynamic derivatives; Artificial neural network; Bridge aerodynamics; Bridges; Motion-induced forces
GND Keyword:Aerodynamik; Ingenieurwissenschaften; Bridge
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
BKL-Classification:56 Bauwesen
Licence (German):License Logo Zweitveröffentlichung
Note:
This is the pre-peer reviewed version of the following article: 
https://www.sciencedirect.com/science/article/abs/pii/S0045794920300018?via%3Dihub, https://doi.org/10.1016/j.compstruc.2020.106198