Utilizing regression models to find functions for determining ripping production based on laboratory tests. (December 2017)

• Record Type: Journal Article
• Authors: Mohamad, Edy Tonnizam; Armaghani, Danial Jahed; Mahdyar, Amir; Komoo, Ibrahim; Kassim, Khairul Anuar; Abdullah, Arham; Majid, Muhd Zaimi Abd
• Is Part Of: Measurement. Volume 111(2017)
• Page Start: 216
• Page End: 225
• Publication Date: 2017-12
• Subjects: Ripping production -- Laboratory test -- Simple regression analysis -- Linear multiple regression -- Non-linear multiple regression; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.07.035
• Languages: English
• ISSNs: 0263-2241

Abstract

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.