Prediction of aeroelastic response of bridge decks using artificial neural networks. (15th April 2020) - Abbas, Tajammal; Kavrakov, Igor; Morgenthal, Guido; Lahmer, Tom
Highlights: Framework to predict motion-induced aerodynamic forces using ANN. Quality assessment of predicted time histories using comparison metrics. ANN training from forced vibration tests and prediction for an ambient input. Trained ANN for aerodynamic forcing coupled with structural model. Abstract: 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 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.