TY  - INPR
A1  - Abbas, Tajammal
A1  - Kavrakov, Igor
A1  - Morgenthal, Guido
A1  - Lahmer, Tom
T1  - Prediction of aeroelastic response of bridge decks using artificial neural networks
N2  - 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.
KW  - Aerodynamik
KW  - Artificial neural network
KW  - Ingenieurwissenschaften
KW  - Bridge
KW  - Bridge aerodynamics
KW  - Aerodynamic derivatives
KW  - Motion-induced forces
KW  - Bridges
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200225-40974
N1  - 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
ER  - 
TY  - INPR
A1  - Steiner, Maria
A1  - Bourinet, Jean-Marc
A1  - Lahmer, Tom
T1  - An adaptive sampling method for global sensitivity analysis based on least-squares support vector regression
N2  - In the field of engineering, surrogate models are commonly used for approximating the behavior of a physical phenomenon in order to reduce the computational costs. Generally, a surrogate model is created based on a set of training data, where a typical method for the statistical design is the Latin hypercube sampling (LHS). Even though a space filling distribution of the training data is reached, the sampling process takes no information on the underlying behavior of the physical phenomenon into account and new data cannot be sampled in the same distribution if the approximation quality is not sufficient. Therefore, in this study we present a novel adaptive sampling method based on a specific surrogate model, the least-squares support vector regresson. The adaptive sampling method generates training data based on the uncertainty in local prognosis capabilities of the surrogate model - areas of higher uncertainty require more sample data. The approach offers a cost efficient calculation due to the properties of the least-squares support vector regression. The opportunities of the adaptive sampling method are proven in comparison with the LHS on different analytical examples. Furthermore, the adaptive sampling method is applied to the calculation of global sensitivity values according to Sobol, where it shows faster convergence than the LHS method. With the applications in this paper it is shown that the presented adaptive sampling method improves the estimation of global sensitivity values, hence reducing the overall computational costs visibly.
KW  - Approximation
KW  - Sensitivitätsanalyse
KW  - Abtastung
KW  - Surrogate models
KW  - Least-squares support vector regression
KW  - Adaptive sampling method
KW  - Global sensitivity analysis
KW  - Sampling
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181218-38320
N1  - This is the pre-peer reviewed version of the following article: https://www.sciencedirect.com/science/article/pii/S0951832017311808, which has been published in final form at https://doi.org/10.1016/j.ress.2018.11.015.
SP  - 1
EP  - 33
ER  -