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Application of machine learning algorithms for predicting the dynamic stiffness of rail pads based on static stiffness and operating conditions

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  • معلومة اضافية
    • Contributors:
      Universidad de Cantabria
    • بيانات النشر:
      MDPI
    • الموضوع:
      2025
    • Collection:
      Universidad de Cantabria: UCrea
    • نبذة مختصرة :
      The vertical stiffness of railway tracks is crucial for ensuring safe and efficient rail transport. Rail-pad dynamic stiffness is a key component influencing track performance. Determining the dynamic stiffness of rail pads poses a challenge because it depends not only on the material and geometry of the rail pad but also on the testing conditions, due to the non-linear material response. To address this issue, a methodology is proposed in this paper to estimate dynamic stiffness using static stiffness measurements. This approach enables the prediction of dynamic stiffness for different situations from a single laboratory test. This study further examines whether this correlation remains valid for different types of rail pads, even when their mechanical behavior has been degraded by temperature, wear, or chemical agents. Experiments were conducted under varying temperatures and on rail pads that underwent mechanical and chemical degradation. The analysis assesses the validity of the static-to-dynamic stiffness correlation under degraded conditions and investigates the influence of each testing condition on the ability to estimate dynamic stiffness from static stiffness and operational parameters. The findings provide insights into the reliability of this predictive model and highlight the impact of degradation mechanisms on the dynamic behavior of rail pads. This research enhances the understanding of rail pad performance and offers a practical approach for evaluating dynamic stiffness. By considering all of the variables used in the analysis, the approach achieves R² values of up to 0.99, which carries significant implications for track design and maintenance. ; This publication is part of the R&D&I project PID2021-128031OB-I00, funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.
    • Relation:
      https://doi.org/10.3390/app15158310; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128031OB-I00/ES/DESARROLLO DE UN SISTEMA DE MONITORIZACION AUTOMATICO DE LINEAS FERROVIARIAS DE ALTA VELOCIDAD MEDIANTE ALGORITMOS MACHINE LEARNING Y DE SIMULACION NUMERICA/; https://hdl.handle.net/10902/39459
    • الرقم المعرف:
      10.3390/app15158310
    • الدخول الالكتروني :
      https://hdl.handle.net/10902/39459
      https://doi.org/10.3390/app15158310
    • Rights:
      © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). ; http://creativecommons.org/licenses/by/4.0/ ; openAccess
    • الرقم المعرف:
      edsbas.2F360506