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Neural Computing and Applications
Published in: Neural Computing and Applications 11/2018

22-03-2017 | Original Article

Uniaxial compressive strength prediction through a new technique based on gene expression programming

Authors: Danial Jahed Armaghani, Vali Safari, Ahmad Fahimifar, Mohd For Mohd Amin, Masoud Monjezi, Mir Ahmad Mohammadi

Published in: Neural Computing and Applications | Issue 11/2018

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Abstract

Proper estimation of rock strength is a critical task for evaluation and design of some geotechnical applications such as tunneling and excavation. Uniaxial compressive strength (UCS) test can be measured directly in the laboratory; nevertheless, the direct UCS determination is time-consuming and expensive. In this study, feasibility of gene expression programming (GEP) model in indirect determination of UCS values of sandstone rock samples is examined. In this regard, several laboratory tests including Brazilian test, density test, slake durability test and UCS test were conducted on 47 samples of sandstone which were collected from the Dengkil, Malaysia. Considering multiple inputs, several GEP models were constructed to estimate UCS of the rock and finally, the best GEP model was selected. In order to indicate capability of the proposed GEP model, linear multiple regression (LMR) was also performed. It was found that the GEP model is superior to LMR one in terms of applied performance indices. Based on coefficient of determination (R 2) of testing datasets, by proposing GEP model, it can be improved from 0.930 (which was obtained by LMR model) to 0.965. As a result, it is concluded that the proposed models in this study, could be utilized to estimate UCS of similar rock type in practice.
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Metadata
Title
Uniaxial compressive strength prediction through a new technique based on gene expression programming
Authors
Danial Jahed Armaghani
Vali Safari
Ahmad Fahimifar
Mohd For Mohd Amin
Masoud Monjezi
Mir Ahmad Mohammadi
Publication date
22-03-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 11/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2939-2

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