Engineering and Applied Sciences

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Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods

Received: 15 August 2019    Accepted: 06 September 2019    Published: 20 September 2019
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Abstract

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.

DOI 10.11648/j.eas.20190404.13
Published in Engineering and Applied Sciences (Volume 4, Issue 4, August 2019)
Page(s) 84-92
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Bayesian Inference, Optimization, Markov Estimator, Parameter Identification, Inverse Problem, Prestressed Concrete Catenary Poles

References
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[6] 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.
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[18] 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.
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Author Information
  • Institution of Structural Mechanics, Bauhaus University Weimar, Weimar, Germany

  • Institution of Structural Mechanics, Bauhaus University Weimar, Weimar, Germany

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    Feras Alkam, Tom Lahmer. (2019). Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods. Engineering and Applied Sciences, 4(4), 84-92. https://doi.org/10.11648/j.eas.20190404.13

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    Feras Alkam; Tom Lahmer. Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods. Eng. Appl. Sci. 2019, 4(4), 84-92. doi: 10.11648/j.eas.20190404.13

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    Feras Alkam, Tom Lahmer. Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods. Eng Appl Sci. 2019;4(4):84-92. doi: 10.11648/j.eas.20190404.13

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  • @article{10.11648/j.eas.20190404.13,
      author = {Feras Alkam and Tom Lahmer},
      title = {Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods},
      journal = {Engineering and Applied Sciences},
      volume = {4},
      number = {4},
      pages = {84-92},
      doi = {10.11648/j.eas.20190404.13},
      url = {https://doi.org/10.11648/j.eas.20190404.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.eas.20190404.13},
      abstract = {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.},
     year = {2019}
    }
    

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    T1  - Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods
    AU  - Feras Alkam
    AU  - Tom Lahmer
    Y1  - 2019/09/20
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    N1  - https://doi.org/10.11648/j.eas.20190404.13
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    JF  - Engineering and Applied Sciences
    JO  - Engineering and Applied Sciences
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    UR  - https://doi.org/10.11648/j.eas.20190404.13
    AB  - 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.
    VL  - 4
    IS  - 4
    ER  - 

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