Group Method of Data Handling Neural Networks (GMDH-NN) in predicting the unconfined compressive strength of two mine tailings matrices for potential use in sustainable road base construction in cold regions
Ali A. Mahmood, Maria Elektorowicz
In the proceedings of: GeoSaskatoon 2023: 76th Canadian Geotechnical ConferenceSession: Case Studies 2
Please include this code when submitting a data update: GEO2023_139
Access this article:
Canadian Geotechnical Society members can access to this article, along with all other Canadian Geotechnical Conference proceedings, in the Member Area. Conference proceedings are also available in many libraries.
Cite this article:
Mahmood, Ali A., Elektorowicz, Maria (2023) Group Method of Data Handling Neural Networks (GMDH-NN) in predicting the unconfined compressive strength of two mine tailings matrices for potential use in sustainable road base construction in cold regions in GEO2023. Ottawa, Ontario: Canadian Geotechnical Society.
@inproceedings{Mahmood_GEO2023_139,
author = {{Mahmood, Ali A.}, {Elektorowicz, Maria}}
title = {Group Method of Data Handling Neural Networks (GMDH-NN) in predicting the unconfined compressive strength of two mine tailings matrices for potential use in sustainable road base construction in cold regions }
booktitle = {Proceedings of the 76th Canadian Geotechnical Conference}
year = {2023}
organization = {The Canadian Geotechnical Society},
address = {Ottawa, Canada} }
title = {Group Method of Data Handling Neural Networks (GMDH-NN) in predicting the unconfined compressive strength of two mine tailings matrices for potential use in sustainable road base construction in cold regions }
booktitle = {Proceedings of the 76th Canadian Geotechnical Conference}
year = {2023}
organization = {The Canadian Geotechnical Society},
address = {Ottawa, Canada} }