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Prediction of railway track geometry defects: a case study
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-3266-2434
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Trafikverket.
School of Fundamental Sciences, Massey University, Palmerston North, New Zealand.
2019 (English)In: Structure and Infrastructure Engineering, ISSN 1573-2479, E-ISSN 1744-8980Article in journal (Refereed) Epub ahead of print
Abstract [en]

The aim of this study has been to develop a data-driven analytical methodology for prediction of isolated track geometry defects, based on the measurement data obtained from a field study. Within the study, a defect-based model has been proposed to identify the degradation pattern of isolated longitudinal level defects. The proposed model considered the occurrence of shock events in the degradation path. Furthermore, the effectiveness of tamping intervention in rectifying the longitudinal level defects was analysed. The results show that the linear model is an appropriate choice for modelling the degradation pattern of longitudinal level defects. In addition, a section-based model has been developed using binary logistic regression to predict the probability of occurrence of isolated defects associated with track sections. The model considered the standard deviation and kurtosis of longitudinal level as explanatory variables. It has been found that the kurtosis of the longitudinal level is a statistically significant predictor of the occurrence of isolated longitudinal level defects in a given track section. The validation results show that the proposed binary logistic regression model can be used to predict the occurrence of isolated defects in a track section.

Place, publisher, year, edition, pages
Taylor & Francis, 2019.
Keywords [en]
Railway track maintenance, geometry defect, degradation, prediction, intervention limit, linear regression, binary logistic regression, shock events, tamping
National Category
Engineering and Technology Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-76538DOI: 10.1080/15732479.2019.1679193ISI: 000494228200001OAI: oai:DiVA.org:ltu-76538DiVA, id: diva2:1366253
Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2019-11-22

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