Volume 4, Issue 4, August 2019, Page: 84-92
Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods
Feras Alkam, Institution of Structural Mechanics, Bauhaus University Weimar, Weimar, Germany
Tom Lahmer, Institution of Structural Mechanics, Bauhaus University Weimar, Weimar, Germany
Received: Aug. 15, 2019;       Accepted: Sep. 6, 2019;       Published: Sep. 20, 2019
DOI: 10.11648/j.eas.20190404.13      View  167      Downloads  66
Prestressed concrete poles nowadays are widely used in supporting the catenary cables of train systems. Compared to their importance to the functionality of the train system, this type of structures have not yet received adequate attention from researchers. We have started tracing the changes in the dynamic behavior of these poles caused by the train passing and the degradation of the materials over a long-time period. In this aim, we installed a structural monitoring system on three of them along one of the high-speed train tracks in Germany. The efficient analysis of the recorded measurements by this system requires a well-known data covering the real material properties of the given structures considering uncertainties of the different parameters. In this paper, we inversely identify the material properties of the poles using deterministic and probabilistic approaches based on the experimental measurements of a full-scale structure and Finite Elements Models. In the deterministic approach, the parameters are identified using the simplex optimization algorithm. Uncertainty of the identified parameters is quantified using a Markov Estimator. In the probabilistic approach, Bayesian inference is utilized for better estimation of the probability distribution of the parameters. Both approaches are suitable for the estimation of mean values of the parameters. The Bayesian method, even though computationally more demanding, is additionally suitable for determining the probability distributions and quantifying the uncertainties of the identified parameters and the correlations between each pair of them. The results show the efficiency of each approach to identify the parameters of the poles. For a rough estimation of the mean values, we recommend the deterministic approach as a simple tool. Conversely, the Bayesian approach is recommended for more detailed and accurate estimation.
Bayesian Inference, Optimization, Markov Estimator, Parameter Identification, Inverse Problem, Prestressed Concrete Catenary Poles
To cite this article
Feras Alkam, Tom Lahmer, Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods, Engineering and Applied Sciences. Vol. 4, No. 4, 2019, pp. 84-92. doi: 10.11648/j.eas.20190404.13
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Kouroussis, G., Verlinden, O., Connolly, D. P., and Forde, M. C. (2014). “Estimation of railway vehicle speed using ground vibration measurements.” 21st International Congress on Sound and Vibration (ICSV21), Beijing, China, 1–8 (July).
Connolly, D., Kouroussis, G., Woodward, P., Costa, P. A., Verlinden, O., and Forde, M. (2014). “Field testing and analysis of high speed rail vibrations.” Soil Dynamics and Earthquake Engineering, 67, 102–118.
Ampunant, P., Kemper, F., Mangerig, I., and Feldmann, M. (2014). “Train–induced aerodynamic pressure and its effect on noise protection walls.” 9th International Conference on Structural Dynamics, A. Cunha, E. Caetano, P. Ribeiro, and G. Muller, eds., EURODYN 2014, 3739–3743 (July).
He, D., Gao, Q., and Zhong, W. (2018). “A numerical method based on the parametric variational principle for simulating the dynamic behavior of the pantograph-catenary system.” Shock and Vibration, 2018.
Van, O. V., Massat, J.-P., and Balmes, E. (2017). “Waves, modes and properties with a major impact on dynamic pantograph-catenary interaction.” Journal of Sound and Vibration, 402, 51–69.
Pombo, J. and Ambrosio, J. (2012). “Influence of pantograph suspension characteristics on the contact quality with the catenary for high speed trains.” Computers & Structures, 110, 32–42.
Aster, R. C., Borchers, B., and Thurber, C. H. (2013). Parameter Estimation and Inverse Problems. Academic Press, second edition.
Beck, J. V. and Arnold, K. J. (1977). Parameter Estimation in Engineering and Science. Wiley, New York.
VanderPlas, J. (2014). “Frequentism and Bayesianism: A python-driven primer.” arXiv preprint arXiv: 1411.5018.
Idier, J. (2013). Bayesian Approach to Inverse Problems. John Wiley & Sons.
Tarantola, A. (2005). Inverse Problem Theory and Methods for Model Parameter Estimation. Society for Industrial and Applied Mathematics.
Lahmer, T. and Rafajłowicz, E. (2017). “On the optimality of harmonic excitation as input signals for the characterization of parameters in coupled piezoelectric and poroelastic problems.” Mechanical Systems and Signal Processing, 90, 399–418.
Calvetti, D. and Somersalo, E. (2007). An Introduction to Bayesian Scientific Computing: Ten Lectures on Subjective Computing, Vol. 2. Springer Science & Business Media.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2014). Bayesian Data Analysis, Vol. 2. Chapman & Hall/CRC Boca Raton, FL, USA, third edition.
Green, P. and Worden, K. (2015). “Bayesian and Markov Chain Monte Carlo Methods for Identifying Nonlinear systems in The Presence of Uncertainty.” Phil. Trans. R. Soc. A, 373(2051), 20140405.
Marzouk, Y. M., Najm, H. N., and Rahn, L. A. (2007). “Stochastic Spectral Methods for Efficient Bayesian Solution of Inverse Problems.” Journal of Computational Physics, 224 (2), 560–586.
Ching, J. and Chen, Y.-C. (2007). “Transitional Markov Chain Monte Carlo Methods for Bayesian Model Updating, Model Class Selection, and Model Averaging.” Journal of Engineering Mechanics, 133 (7), 816–832.
Göbel, L., Mucha, F., Kavrakov, I., Abrahamczyk, L., and Kraus, M. (2018). “Einfluss realer Materialeigenschaften auf numerische Modellvorhersagen: Fallstudie Betonmast.” Bautechnik, 95 (1), 111–122.
Reynders, E., Schevenels, M., and De Roeck, G. (2014). “MACEC 3.3: A MATLAB toolbox for experimental and operational modal analysis.
Batikha, M. and Alkam, F. (2015). “The Effect of Mechanical Properties of Masonry on the behavior of FRP-strengthened Masonry-infilled RC Frame under Cyclic Load.” Composite Structures, 134, 513–522.
P. Beverly, ed. (2013). fib Model Code for Concrete Structures 2010. Wiley-VCH Verlag GmbH & Co. KGaA.
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