Scholarly record
AUTOMATED DETERMINATION OF CONSTITUTIVE PARAMETERS USING A METAHEURISTIC OPTIMIZATION METHOD IN SYNTHETIC EXPERIMENT
Abstract
Higher complexity of modern constitutive models often results in an increase of number of input parameters and time requirements for their calibration. Optimization methods provide a possible solution for this problem. A program application based on the metaheuristic optimization method Particle swarm optimization (PSO) is presented in the paper. The application consists of two parts: a subroutine with the PSO algorithm and a subroutine involving a finite element (FE) model of an analyzed construction. The subroutine with PSO governs input parameters modifications and evaluation of the objective function. Modified values of input parameters are then used in the FE model subroutine for calculation. Different alternatives of the PSO method are reviewed and tested in the first part of the paper. In the second part, a synthetic numerical experiment of pressuremeter test is used for automatically driven determination of input parameters for two constitutive models: Mohr ? Coulomb, Hardening soil. The procedure is able to perform fully automatic determination of input parameters values. However, when only single test is used, several combinations of input parameters might get to the same result. Future research will be therefore focused on a combination of multiple measurements (e. g. from different depths) used during the optimization task.
Publication Impact Profile
Publication details
References10
GILBERT, Paul A., 1992. Effect of sampling disturbance on laboratory-measured soil properties. Vicksburg, 52 p.
BENZ, Thomas, 2007. Small-strain stiffness of soils and its numerical consequences. Stuttgart: Inst. für Geotechnik. ISBN 978-392-1837-559.
KENNEDY, J. a EBERHART, R. C. Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks, 4, pp. 1942–1948, Perth, Australia, IEEE Service Center, Piscataway, NJ, 1995.
EBERHART, R. a J. KENNEDY, 1995. A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Piscataway, NJ: IEEE, pp. 39-43. DOI: 10.1109/MHS.1995.494215. ISBN 0-7803-2676-8. http://ieeexplore.ieee.org/document/494215/
SHI, Y. and EBERHART, R. A modified particle swarm optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), pp. 69–73. IEEE, 1998. DOI: 10.1109/ICEC.1998.699146. http://ieeexplore.ieee.org/document/699146/. ISBN 0- 7803-4869-9.
HERIS, M. K. Video Tutorial of PSO implementation in MATLAB. http://yarpiz.com/440/ytea101-particle-swarm-optimization-pso-inmatlab-video-tutorial, 2016.
YANG, Xin-She, 2008. Introduction to mathematical optimization: from linear programming to metaheuristics. Cambridge, UK: Cambridge International Science Publishing. ISBN 978-190-4602-828.
MOLGA, Marcin a Czeslaw SMUTNICKI, 2005. Test functions for optimization needs. [cit. 2018-04-10]. http://new.zsd.iiar.pwr.wroc.pl/files/docs/functions.pdf
BRINKGREVE, R., KUMARSWAMY, S. a SWOLFS, W. Plaxis: Finite Element Code for Soil and Rock Analyses: 2D-Version [user’s Guide]., 2016.
MIČA, L. et al. Numerická analýza pažení stavebních jam. Akademické nakladatelství CERM, vyd. 1. edition, 2011. ISBN 978-80-7204-773-4.
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
