Automatic Detection of Sand Fouling Levels in Railway Tracks Using Supervised Machine Learning: A Case Study from Saudi Arabian Railway
Author:
Funder
Researchers Supporting Project, King Saud University
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-022-07243-0.pdf
Reference39 articles.
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3. Zarembski, A.M.; Palese, J.; Chalupa, M.: Maintenance planning for rail asset management—current practices. TCRP Synthesis of Transit Practice. 151 (2020)
4. Sussmann, T.R.; Ruel, M.; Chrismer, S.M.: Source of ballast fouling and influence considerations for condition assessment criteria. Trans. Res. Record: J. Transp. Res. Board. 2289, 87–94 (2012)
5. Koohmishi, M.; Palassi, M.: Effect of gradation of aggregate and size of fouling materials on hydraulic conductivity of sand-fouled railway ballast. Constr. Build. Mater. 167, 514–523 (2018)
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