Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network

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Original language	English
Pages (from-to)	945-962
Journal	Journal of Adhesion Science and Technology
Volume	33
Issue number	9
Early online date	23 Feb 2019
DOIs	https://doi.org/10.1080/01694243.2019.1571659
Publication status	Published - 2019

Keywords

@article{84d7eedfb4ea41e28c18f13c17f28e5d,

title = "Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network",

keywords = "General engineering and mineral and mining engineering",

author = "Hossein MolaAbasi and Mohammad Saberian and Afshin Kordnaeij and Jousha Omer and Jie Li and Parisa Kharazmi",

year = "2019",

doi = "10.1080/01694243.2019.1571659",

language = "English",

volume = "33",

pages = "945--962",

journal = "Journal of Adhesion Science and Technology",

issn = "0169-4243",

publisher = "Taylor and Francis Ltd.",

number = "9",

}

TY - JOUR

T1 - Predicting the stress-strain behaviour of zeolite-cemented sand based on the unconfined compression test using GMDH type neural network

AU - MolaAbasi, Hossein

AU - Saberian, Mohammad

AU - Kordnaeij, Afshin

AU - Omer, Jousha

AU - Li, Jie

AU - Kharazmi, Parisa

PY - 2019

Y1 - 2019

KW - General engineering and mineral and mining engineering

U2 - 10.1080/01694243.2019.1571659

DO - 10.1080/01694243.2019.1571659

M3 - Article

SN - 0169-4243

VL - 33

SP - 945

EP - 962

JO - Journal of Adhesion Science and Technology

JF - Journal of Adhesion Science and Technology

IS - 9

ER -