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Structure and Infrastructure Engineering
Maintenance, Management, Life-Cycle Design and Performance
Volume 18, 2022 - Issue 2
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Research Article

Bayesian Monte Carlo approach for developing stochastic railway track degradation model using expert-based priors

Mahsa MovagharSchool of Railway Engineering, Iran University of Science and Technology, Tehran, IranORCID Iconhttps://orcid.org/0000-0001-7226-9605
ORCID Icon &
Saeed MohammadzadehSchool of Railway Engineering, Iran University of Science and Technology, Tehran, IranCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-6535-9351
ORCID Icon
Pages 145-166 | Received 06 Jun 2020, Accepted 30 Sep 2020, Published online: 04 Nov 2020

Citations (6)

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Read on this site (1)

Pedro Rodrigues & Paulo F. Teixeira. (2022) Modelling degradation rates of track geometry local defects: Lisbon-Porto line case study. Structure and Infrastructure Engineering 0:0, pages 1-16.
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Articles from other publishers (5)

Tzu-Hao Yan, Mariana De Almeida Costa & Francesco Corman. (2023) Developing and Extending Status Prediction Models for Railway Tracks Based on On-Board Monitoring Data. Transportation Research Record: Journal of the Transportation Research Board 2677:6, pages 708-719.
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Katsuya Kosukegawa, Yasukuni Mori, Hiroki Suyari & Kazuhiko Kawamoto. (2023) Spatiotemporal forecasting of vertical track alignment with exogenous factors. Scientific Reports 13:1.
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Yingying Liao, Lei Han, Haoyu Wang & Hougui Zhang. (2022) Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review. Sensors 22:19, pages 7275.
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Chenzhong Li, Qing He & Ping Wang. (2021) Estimation of railway track longitudinal irregularity using vehicle response with information compression and Bayesian deep learning. Computer-Aided Civil and Infrastructure Engineering 37:10, pages 1260-1276.
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Mahdi Jemmali, Loai Kayed B. Melhim & Fayez Al Fayez. (2022) Real time read-frequency optimization for railway monitoring system. RAIRO - Operations Research 56:4, pages 2721-2749.
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