Predicting uniaxial compressive strength using Support Vector Machine algorithm

Compressive strength is the most important parameter in rock since all loads will be transferred and rest on the rock which is based on the load bearing capacity of rock in compression. However, obtaining the compressive strength or mostly measured, the uniaxial compressive strength (UCS) from the l...

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Main Authors:	Zakaria, Hafedz, Abdullah, Rini Asnida, Ismail, Amelia Ritahani, Amin, Mohd For
Format:	Article
Language:	English
Published:	2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/77297/ http://irep.iium.edu.my/77297/1/Predicting%20uniaxial%20compressive%20strength%20using%20Support%20Vector%20Machine%20algorithm%20.pdf

id	iium-77297
recordtype	eprints
spelling	iium-772972020-01-08T08:20:37Z http://irep.iium.edu.my/77297/ Predicting uniaxial compressive strength using Support Vector Machine algorithm Zakaria, Hafedz Abdullah, Rini Asnida Ismail, Amelia Ritahani Amin, Mohd For QA75 Electronic computers. Computer science Compressive strength is the most important parameter in rock since all loads will be transferred and rest on the rock which is based on the load bearing capacity of rock in compression. However, obtaining the compressive strength or mostly measured, the uniaxial compressive strength (UCS) from the laboratory test requires certain standard and also cost constrain. This paper presents the application of Support Vector Machine (SVM) algorithm to predict the UCS. An algorithm has been tested on a series of rock data using dry density and velocity parameters. The relationship between the dry density, sonic velocity, and UCS was analyzed using RapidMiner Studio software. From the result, it was found that SVM is capable of predicting the missing values with a prediction trend accuracy of 75%. The results obtained and observation made in this study suggests that SVM could be a reliable tool to predict the UCS of a given rock. More robust prediction can be established with bigger sample number. It is worth mentioning, that the program module that has been set up could be used repeatedly for other correlation problems. 2019-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/77297/1/Predicting%20uniaxial%20compressive%20strength%20using%20Support%20Vector%20Machine%20algorithm%20.pdf Zakaria, Hafedz and Abdullah, Rini Asnida and Ismail, Amelia Ritahani and Amin, Mohd For (2019) Predicting uniaxial compressive strength using Support Vector Machine algorithm. Warta Geologi, 45 (1). pp. 13-16. ISSN 0126–5539 E-ISSN 2682-7549
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Zakaria, Hafedz Abdullah, Rini Asnida Ismail, Amelia Ritahani Amin, Mohd For Predicting uniaxial compressive strength using Support Vector Machine algorithm
description	Compressive strength is the most important parameter in rock since all loads will be transferred and rest on the rock which is based on the load bearing capacity of rock in compression. However, obtaining the compressive strength or mostly measured, the uniaxial compressive strength (UCS) from the laboratory test requires certain standard and also cost constrain. This paper presents the application of Support Vector Machine (SVM) algorithm to predict the UCS. An algorithm has been tested on a series of rock data using dry density and velocity parameters. The relationship between the dry density, sonic velocity, and UCS was analyzed using RapidMiner Studio software. From the result, it was found that SVM is capable of predicting the missing values with a prediction trend accuracy of 75%. The results obtained and observation made in this study suggests that SVM could be a reliable tool to predict the UCS of a given rock. More robust prediction can be established with bigger sample number. It is worth mentioning, that the program module that has been set up could be used repeatedly for other correlation problems.
format	Article
author	Zakaria, Hafedz Abdullah, Rini Asnida Ismail, Amelia Ritahani Amin, Mohd For
author_facet	Zakaria, Hafedz Abdullah, Rini Asnida Ismail, Amelia Ritahani Amin, Mohd For
author_sort	Zakaria, Hafedz
title	Predicting uniaxial compressive strength using Support Vector Machine algorithm
title_short	Predicting uniaxial compressive strength using Support Vector Machine algorithm
title_full	Predicting uniaxial compressive strength using Support Vector Machine algorithm
title_fullStr	Predicting uniaxial compressive strength using Support Vector Machine algorithm
title_full_unstemmed	Predicting uniaxial compressive strength using Support Vector Machine algorithm
title_sort	predicting uniaxial compressive strength using support vector machine algorithm
publishDate	2019
url	http://irep.iium.edu.my/77297/ http://irep.iium.edu.my/77297/1/Predicting%20uniaxial%20compressive%20strength%20using%20Support%20Vector%20Machine%20algorithm%20.pdf
first_indexed	2023-09-18T21:49:02Z
last_indexed	2023-09-18T21:49:02Z
_version_	1777413644470976512

Predicting uniaxial compressive strength using Support Vector Machine algorithm

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