Predicting uniaxial compressive strength using Support Vector Machine algorithm

Warta Geologi, Vol. 45, No. 1
Author : Hafedz Zakaria, Rini Asnida Abdullah, Amelia Ritahani Ismail, Mohd For Mohd Amin
Publication : Warta Geologi
Page : 13 - 16
Volume Number : 45
Year : 2019

Warta Geologi, Vol. 45, No. 1, March 2019, pp. 13–16


Predicting uniaxial compressive strength using Support
Vector Machine algorithm


Hafedz Zakaria1, Rini Asnida Abdullah2,*,
Amelia Ritahani Ismail3, Mohd For Mohd Amin2

1Public Works Department of Malaysia, Jalan
Sultan Salahuddin, 50582 Kuala Lumpur, Malaysia

2Faculty of Civil Engineering, Universiti
Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia

3Kulliyah of ICT, International Islamic
University, 50728 Kuala Lumpur, Malaysia

*Corresponding author email address:


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.


unconfined compressive strength, dry density, sonic wave velocity, support
vector machine


0126–5539; e-ISSN 2682-7549