Abstract
In this paper we present a new approach for solving identification problems based on a novel combination of computer vision techniques, Bayesian state estimation and finite element method. Using our approach we solved two identification problems for a laboratory-scale aluminum frame. In the first problem, we recursively estimated the elastic modulus of the frame material. In the second problem, for the known elastic constant, we identified sequentially the position of a quasi-static concentrated load.
Keywords:
identification problems, Bayesian state estimation, particle filtering, computer vision, digital image correlation, finite element methodReferences
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[12] T. Uhl, P. Kohut, K. Holak, K. Krupiński. Vision based condition assessment of structures. Journal of Physics: Conference Series, 305: 12043–12052, IOP Publishing, 2011.
[2] J. Ching, J.L. Beck, K.A. Porter, R. Shaikhutdinov. Bayesian state estimation method for nonlinear systems and its application to recorded seismic response. Journal of Engineering Mechanics, 132(4): 396–410, 2006.
[3] A. Doucet, N. De Freitas, N. Gordon [Eds.]. Sequential Monte Carlo methods in practice. Springer Verlag, 2001.
[4] T. Gajewski, T. Garbowski. Calibration of concrete parameters based on digital image correlation and inverse analysis. Archives of Civil and Mechanical Engineering, 14(1): 170–180, 2014.
[5] N.J. Gordon, D.J. Salmond, A.F.M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing, IEE Proceedings F, 140: 107–113, IET, 1993.
[6] G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1): 1–25, 1996.
[7] H.A. Nasrellah, C.S. Manohar. Finite element method based Monte Carlo filters for structural system identification. Probabilistic Engineering Mechanics, 26(2): 294–307, 2011.
[8] S. Russel, P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, 3rd Ed., 2010.
[9] M.A. Sutton, J.J. Orteu, H.W. Schreier. Image correlation for shape, motion and deformation measurements. Springer, 2009.
[10] M.A. Sutton, W.J. Wolters, W.H. Peters, W.F. Ranson, S.R. McNeill. Determination of displacements using an improved digital correlation method. Image and Vision Computing, 1(3): 133–139, 1983.
[11] M. Tekieli, M. Słoński. Computer vision based method for real time material and structure parameters estimation using digital image correlation, particle filtering and finite element method. In Artificial Intelligence and Soft Computing, volume 7894 of Lecture Notes in Computer Science, pages 624–633, Springer, 2013.
[12] T. Uhl, P. Kohut, K. Holak, K. Krupiński. Vision based condition assessment of structures. Journal of Physics: Conference Series, 305: 12043–12052, IOP Publishing, 2011.
