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Railway Bridge Management System Based on Visual Inspections with Semi-Markov Continuous Time Process

  • Railroad Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

This article describes a model for predicting the degradation of in-service railway bridges based on a semi-Markov continuous time process. This model relies on the history of inspections of 588 bridges located on a heavy-haul railway line in Brazil, between 2016 and 2020. A dedicated computational tool developed in Matlab allows the automated data processing. A parametric study is performed to understand which factors derived from the bridge structural characteristics, as well as operational and environmental factors, most influence the deterioration model. The type of material proves to be a decisive factor and therefore two specific prediction models are stablished, one for concrete bridges and other to steel bridges. The prediction models have an efficiency equal to 93.7%, for concrete bridges, and 95.1% for steel bridges. Additionally, the analysis of several preventive and corrective maintenance scenarios, specifically for concrete bridges, allows to optimize the condition ratings during the life cycle.

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Acknowledgments

The authors would like to thank the support provided by the railway infrastructure department of the company Rumo Logística in carrying out the research. The second author would like to acknowledge the support of the Base Funding UIDB/04708/2020 and Programmatic Funding UIDP/04708/2020 of the CONSTRUCT (Instituto de I&D em Estruturas e Construções) funded by national funds through the FCT/MCTES (PIDDAC).

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Authors and Affiliations

  1. Rumo Logística, Railway Infrastructure, Curitiba, 82920-030, Brazil

    Fagner Furtado

  2. CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Porto, 4249-015, Portugal

    Diogo Ribeiro

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  1. Fagner Furtado
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  2. Diogo Ribeiro
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Correspondence to Diogo Ribeiro.

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Furtado, F., Ribeiro, D. Railway Bridge Management System Based on Visual Inspections with Semi-Markov Continuous Time Process. KSCE J Civ Eng 27, 233–250 (2023). https://doi.org/10.1007/s12205-022-0387-8

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  • DOI: https://doi.org/10.1007/s12205-022-0387-8

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