A Bayesian Framework for Optimal Experimental Design in Structural Dynamics

  • Costas Argyris
  • Costas PapadimitriouEmail author
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


A Bayesian framework for optimal experimental design in structural dynamics is presented. The optimal design is based on an expected utility function that measures the value of the information arising from alternative experimental designs and takes into account the uncertainties in model parameters and model prediction error. The evaluation of the expected utility function requires a large number of structural model simulations. Asymptotic techniques are used to simplify the expected utility functions under small model prediction error uncertainties, providing insight into the optimal design and drastically reducing the computation effort involved in the evaluation of the multi-dimensional integrals that arise. The framework is demonstrated using the design of sensors for modal identification and is applied to the design of a small number of reference sensors for experiments involving multiple sensor configuration setups accomplished with reference and moving sensors. In contrast to previous formulations, the Bayesian optimal experimental design overcomes the problem of the ill-conditioned Fisher information matrix for small number of reference sensors by exploiting the information in the prior distribution.


Bayesian inference Information entropy Relative entropy Kullback–Leibler divergence Structural dynamics 



This research has been implemented under the “ARISTEIA” Action of the “Operational Programme Education and Lifelong Learning” and was co-funded by the European Social Fund (ESF) and Greek National Resources.


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Copyright information

© The Society for Experimental Mechanics, Inc. 2016

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of ThessalyVolosGreece

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