Utilizing regression models to find functions for determining ripping production based on laboratory tests

Edy Tonnizam Mohamad, Danial Jahed Armaghani, Amir Mahdyar, Ibrahim Komoo, Khairul Anuar Kassim, Arham Abdullah, Muhd Zaimi Abd Majid

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

The selection of suitable overburden loosening method has a crucial importance in several applications of geotechnical engineering. Factors such as rock properties and environmental constrains play a significant role in the selection of the needed equipment for overburden loosening. This paper presents several new models/equations for prediction of ripping production (Q) using rock material properties. To this end, three sites in Malaysia were selected and a total of 52 direct ripping tests were conducted in Johor state on sandstone and shale rock types. In addition, using the collected block samples, point load test, Brazilian test, slake-durability test, p-wave velocity test and uniaxial compressive strength test were also carried out in the laboratory. Numerous equations have been proposed to predict Q considering simple regression, linear multiple regression (LMR), and non-linear multiple regression (NLMR) models. Simple regression analysis indicated that the relationships between rock material properties and Q were meaningful and acceptable. Furthermore, both LMR and NLMR equations indicated similar performance capacity in predicting Q. Nevertheless; the use of NLMR equations resulted in prediction performance with higher accuracy in estimating Q compared to LMR equations.

Original languageEnglish
Pages (from-to)216-225
Number of pages10
JournalMeasurement: Journal of the International Measurement Confederation
Volume111
DOIs
Publication statusPublished - 1 Dec 2017

Fingerprint

regression analysis
Regression Model
Rocks
regression
Multiple Linear Regression
Nonlinear Regression
Multiple Regression
Materials properties
rocks
Material Properties
Geotechnical engineering
Shale
Sandstone
Linear regression
Regression analysis
Compressive strength
Simple Linear Regression
Loads (forces)
Compressive Strength
Durability

Keywords

  • Laboratory test
  • Linear multiple regression
  • Non-linear multiple regression
  • Ripping production
  • Simple regression analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Education
  • Condensed Matter Physics
  • Applied Mathematics

Cite this

Utilizing regression models to find functions for determining ripping production based on laboratory tests. / Mohamad, Edy Tonnizam; Armaghani, Danial Jahed; Mahdyar, Amir; Komoo, Ibrahim; Kassim, Khairul Anuar; Abdullah, Arham; Majid, Muhd Zaimi Abd.

In: Measurement: Journal of the International Measurement Confederation, Vol. 111, 01.12.2017, p. 216-225.

Research output: Contribution to journalArticle

Mohamad, Edy Tonnizam ; Armaghani, Danial Jahed ; Mahdyar, Amir ; Komoo, Ibrahim ; Kassim, Khairul Anuar ; Abdullah, Arham ; Majid, Muhd Zaimi Abd. / Utilizing regression models to find functions for determining ripping production based on laboratory tests. In: Measurement: Journal of the International Measurement Confederation. 2017 ; Vol. 111. pp. 216-225.
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