Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models

Shengpeng Hao and Thomas Pabst

Article (2022)

An external link is available for this item

Department:	Department of Civil, Geological and Mining Engineering
PolyPublie URL:	https://publications.polymtl.ca/50852/
Journal Title:	ACTA Geotechnica (vol. 17, no. 4)
Publisher:	Springer-Verlag
DOI:	10.1007/s11440-022-01472-1
Official URL:	https://doi.org/10.1007/s11440-022-01472-1
Date Deposited:	18 Apr 2023 14:58
Last Modified:	08 Apr 2025 07:18

Cite in APA 7:	Hao, S., & Pabst, T. (2022). Prediction of CBR and resilient modulus of crushed waste rocks using machine learning models. ACTA Geotechnica, 17(4), 1383-1402. https://doi.org/10.1007/s11440-022-01472-1
APA 7 (HTML)ASCII CitationAtomBibTeXDOIsDublin CoreEndNote (.ENW)MARC (ISO 2709/UTF-8) (.MRC)MARC (UTF-8) (.MRK8)MARC XMLMPEG-21 DIDLMultiline CSVOAI-ORE Resource Map (Atom Format)OPENAIREOpenURL ContextObjectOpenURL ContextObject in SpanRDF+N-TriplesRDF+N3RDF+XMLReferReference Manager (.RIS)Simple Metadata

Statistics

Repository Staff Only