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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 106
PROCEEDINGS OF THE TWELFTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY
Edited by: B.H.V. Topping and P. Iványi
Paper 173

An Expensive Optimization based Computational Intelligence Method for Railway Track Parameter Identification

A. Núñez, M. Oregui and M. Molodova

Section of Road and Railway Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, The Netherlands

Full Bibliographic Reference for this paper
A. Núñez, M. Oregui, M. Molodova, "An Expensive Optimization based Computational Intelligence Method for Railway Track Parameter Identification", in B.H.V. Topping, P. Iványi, (Editors), "Proceedings of the Twelfth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 173, 2014. doi:10.4203/ccp.106.173
Keywords: railway track model, railway track parameter identification by hammer test, particle swarm optimization for expensive multiobjective optimization..

Summary
To reduce the subjectivity in the identification of the railway tracks parameters, in this paper an expensive optimization procedure is proposed using a modified version of particle swarm optimization to cope with multiobjective optimization with subjective decisions. From the optimization point of view, the railway track model is a black-box from where given a set of in-service railway track parameters a nonparametric response is provided. The final goal is to identify track parameters whose simulation better fits with real measurements. The simulation is computationally expensive and only sixteen licences of the software LS-Dyna are used in multiple cores. The optimization algorithm searches for the set of possible track parameters that provide the best performance in terms of multiple objectives, aiming to (1) mathematically determine a good-fit while dealing with a global optimal search over a non-convex optimization problem, (2) speed-up the fitting process, (3) include multiple objectives such as robustness to cope with a statistically reliable number of different measurements for one track, a good fit of the main seven characteristics of the railway track, and the overall good fit of the non-parametric representation of the track response, and (4) include subjectivity of the expert by selecting the best solutions from an alpha-pareto solution set.

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