Uniaxial compressive strength prediction through a new technique based on gene expression programming
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 ex...
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| Format: | Article |
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Springer London
2018
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| _version_ | 1796862650068500480 |
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| author | Jahed Armaghani, D. Safari, V. Fahimifar, A. Mohd. Amin, M. F. Monjezi, M. Mohammadi, M. A. |
| author_facet | Jahed Armaghani, D. Safari, V. Fahimifar, A. Mohd. Amin, M. F. Monjezi, M. Mohammadi, M. A. |
| author_sort | Jahed Armaghani, D. |
| collection | ePrints |
| description | 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 (R2) 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. |
| first_indexed | 2024-03-05T20:14:51Z |
| format | Article |
| id | utm.eprints-77211 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:14:51Z |
| publishDate | 2018 |
| publisher | Springer London |
| record_format | dspace |
| spelling | utm.eprints-772112020-10-11T06:04:02Z http://eprints.utm.my/77211/ Uniaxial compressive strength prediction through a new technique based on gene expression programming Jahed Armaghani, D. Safari, V. Fahimifar, A. Mohd. Amin, M. F. Monjezi, M. Mohammadi, M. A. TA Engineering (General). Civil engineering (General) 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 (R2) 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. Springer London 2018-12-01 Article PeerReviewed Jahed Armaghani, D. and Safari, V. and Fahimifar, A. and Mohd. Amin, M. F. and Monjezi, M. and Mohammadi, M. A. (2018) Uniaxial compressive strength prediction through a new technique based on gene expression programming. Neural Computing and Applications, 30 (11). pp. 3523-3532. ISSN 0941-0643 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015863353&doi=10.1007%2fs00521-017-2939-2&partnerID=40&md5=301da71056973896939ad6215ec00f88 DOI:10.1007/s00521-017-2939-2 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Jahed Armaghani, D. Safari, V. Fahimifar, A. Mohd. Amin, M. F. Monjezi, M. Mohammadi, M. A. Uniaxial compressive strength prediction through a new technique based on gene expression programming |
| title | Uniaxial compressive strength prediction through a new technique based on gene expression programming |
| title_full | Uniaxial compressive strength prediction through a new technique based on gene expression programming |
| title_fullStr | Uniaxial compressive strength prediction through a new technique based on gene expression programming |
| title_full_unstemmed | Uniaxial compressive strength prediction through a new technique based on gene expression programming |
| title_short | Uniaxial compressive strength prediction through a new technique based on gene expression programming |
| title_sort | uniaxial compressive strength prediction through a new technique based on gene expression programming |
| topic | TA Engineering (General). Civil engineering (General) |
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