Data-driven clay-fouled ballast permeability assessment using analytical-numerical and machine learning approaches

Author:

Koohmishi MehdiORCID,Guo YunlongORCID

Funder

European Commission

Engineering and Physical Sciences Research Council

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Transportation,Civil and Structural Engineering

Reference44 articles.

1. Using a seismic survey to measure the shear modulus of clean and fouled ballast;Anbazhagan;Geomech. Geoeng.: Int. J.,2010

2. Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations;Araya;Water Resour Res,2019

3. AREMA. (2010). Manual for railway engineering, Vol. 1: Track, Ch. 1: Roadway and Ballast: American Railroad Engineering and Maintenance of Way Association (AREMA), Washington, D.C.

4. Prediction of hydraulic conductivity of porous granular media by establishment of random forest algorithm;Javad Azarhoosh;Constr Build Mater,2023

5. Random forests;Breiman;Machine learning,2001

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