@article{KavrakovKareemMorgenthal,
  author    = {Kavrakov, Igor and Kareem, Ahsan and Morgenthal, Guido},
  title     = {Comparison Metrics for Time-histories: Application to Bridge Aerodynamics},
  doi       = {10.25643/bauhaus-universitaet.4186},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200625-41863},
  pages     = {28},
  abstract  = {Wind effects can be critical for the design of lifelines such as long-span bridges. The existence of a significant number of aerodynamic force models, used to assess the performance of bridges, poses an important question regarding their comparison and validation. This study utilizes a unified set of metrics for a quantitative comparison of time-histories in bridge aerodynamics with a host of characteristics. Accordingly, nine comparison metrics are included to quantify the discrepancies in local and global signal features such as phase, time-varying frequency and magnitude content, probability density, nonstationarity and nonlinearity. Among these, seven metrics available in the literature are introduced after recasting them for time-histories associated with bridge aerodynamics. Two additional metrics are established to overcome the shortcomings of the existing metrics. The performance of the comparison metrics is first assessed using generic signals with prescribed signal features. Subsequently, the metrics are applied to a practical example from bridge aerodynamics to quantify the discrepancies in the aerodynamic forces and response based on numerical and semi-analytical aerodynamic models. In this context, it is demonstrated how a discussion based on the set of comparison metrics presented here can aid a model evaluation by offering deeper insight. The outcome of the study is intended to provide a framework for quantitative comparison and validation of aerodynamic models based on the underlying physics of fluid-structure interaction. Immediate further applications are expected for the comparison of time-histories that are simulated by data-driven approaches.},
  subject      = {Ingenieurwissenschaften},
  language  = {en}
}
@unpublished{AbbasKavrakovMorgenthaletal.,
  author    = {Abbas, Tajammal and Kavrakov, Igor and Morgenthal, Guido and Lahmer, Tom},
  title     = {Prediction of aeroelastic response of bridge decks using artificial neural networks},
  doi       = {10.25643/bauhaus-universitaet.4097},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200225-40974},
  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.},
  subject      = {Aerodynamik},
  language  = {en}
}