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B. Laurent¹, O. François¹, A. Panunzio², S. Simon², M. Essadeq³, D. Drago³, F. Parrennes⁴ and M. Cogne⁵

¹Gustave Eiffel University, COSYS-LEOST, Villeneuve-d’Ascq, France
², IMDM Group, Paris, France
³, Systra, Saint Denis, France
⁴, RATP, La Pleine Daint Denis, France
⁵, Société des Grands Projets, Saint Denis, France

doi:10.4203/ccc.15.17.7

B. Laurent, O. François, A. Panunzio, S. Simon, M. Essadeq, D. Drago, F. Parrennes, M. Cogne, "A Mechanico-Statistical Approach for the Assessment of Broken Rail Prevention on the Grand Paris Express", in J. Pombo, (Editor), "Proceedings of the Seventh International Conference on Railway Technology: Research, Development and Maintenance ", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 15, Paper 17.7, 2026, doi:10.4203/ccc.15.17.7

Keywords: railway maintenance, broken rail prevention, predictive maintenance, maintenance optimisation, statistical modelling, mechanical modelling, Bayesian networks, decision support.

Abstract

Broken rail prevention is a major challenge for any railway infrastructure manager. It requires the definition of a consistent and rational strategy for rail inspection and preventive maintenance. The determination of optimal parameters is often facilitated by the implementation of digital twins, which make it possible to evaluate the behaviour of decision indicators under different inspection and maintenance configurations. These maintenance strategy assessment tools are generally based on operational feedback data, allowing the dynamics of defect initiation and propagation to be characterized in various contexts. In the case of the future Grand Paris Express, no such data are available, as no network of comparable scale and operating conditions has yet been operated. To overcome this limitation, this paper proposes a mechanico-statistical approach for initializing rail lifetime parameters under different contexts and subsequently running simulations for various operating, inspection, and maintenance parameter sets, relying on the generic VirMaLab virtual maintenance laboratory framework based on Bayesian network formalism.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 15 PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 17.7 A Mechanico-Statistical Approach for the Assessment of Broken Rail Prevention on the Grand Paris Express B. Laurent¹, O. François¹, A. Panunzio², S. Simon², M. Essadeq³, D. Drago³, F. Parrennes⁴ and M. Cogne⁵ ¹Gustave Eiffel University, COSYS-LEOST, Villeneuve-d’Ascq, France ², IMDM Group, Paris, France ³, Systra, Saint Denis, France ⁴, RATP, La Pleine Daint Denis, France ⁵, Société des Grands Projets, Saint Denis, France doi:10.4203/ccc.15.17.7 Full Bibliographic Reference for this paper B. Laurent, O. François, A. Panunzio, S. Simon, M. Essadeq, D. Drago, F. Parrennes, M. Cogne, "A Mechanico-Statistical Approach for the Assessment of Broken Rail Prevention on the Grand Paris Express", in J. Pombo, (Editor), "Proceedings of the Seventh International Conference on Railway Technology: Research, Development and Maintenance ", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 15, Paper 17.7, 2026, doi:10.4203/ccc.15.17.7 Keywords: railway maintenance, broken rail prevention, predictive maintenance, maintenance optimisation, statistical modelling, mechanical modelling, Bayesian networks, decision support. Abstract Broken rail prevention is a major challenge for any railway infrastructure manager. It requires the definition of a consistent and rational strategy for rail inspection and preventive maintenance. The determination of optimal parameters is often facilitated by the implementation of digital twins, which make it possible to evaluate the behaviour of decision indicators under different inspection and maintenance configurations. These maintenance strategy assessment tools are generally based on operational feedback data, allowing the dynamics of defect initiation and propagation to be characterized in various contexts. In the case of the future Grand Paris Express, no such data are available, as no network of comparable scale and operating conditions has yet been operated. To overcome this limitation, this paper proposes a mechanico-statistical approach for initializing rail lifetime parameters under different contexts and subsequently running simulations for various operating, inspection, and maintenance parameter sets, relying on the generic VirMaLab virtual maintenance laboratory framework based on Bayesian network formalism. download the full-text of this paper (PDF, 14 pages, 803 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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