Article

Geology for civil engineers / A. C. McLean, C. D. Gribble

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Reimpresiones de 1992 Incluye bibliografía e índice

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... They also provided a chart classifying the excavation methods in detail. McLean and Gribble (1985) used the results of UCS and Schmidt hammer rebound number in predicting rippability of rock masses. A classification system using a number of material and mass properties of rock,such as Schmidt hammer hardness, seismic P-wave velocity, the point load index (or uniaxial compressive strength) and the mean spacing of discontinuity is designed by Karpuz (1990) and Basarir and Karpuz (2004) to provide an apt description of Coal Measures and marls for use in lignite mines. ...
Article
Full-text available
In response to the environmental restrictions and the blasting problems, ripping method as a surface excavation method is the most commonly-used in construction of many civil engineering systems. So, it is essential to provide a more applicable rippability model that can effectively predict ripping production (Q) in the field. This paper presents several new models/equations for prediction of Q in diverse weathering zones (grade from II to V) based on field observations and in situ tests. To do this, four sites in Johor state, Malaysia were selected and a total of 123 direct ripping tests were carried out on two types of sedimentary rocks, namely, sandstone and shale. Based on literature’s suggestions and possible conducted field works, point load strength index, sonic velocity, Schmidt hammer rebound number and joint spacing were chosen to estimate Q in different weathering zones. Then, simple and multiple regression analyses, namely linear multiple regression (LMR) and non-linear multiple regression (NLMR) were performed to predict Q. The simple regression analysis generally showed an acceptable and meaningful correlation between the Q and input variables. Additionally, a range of 0.582–0.966 was obtained for coefficient of determination (R2) values of developed LMR models while this range was observed from 0.586 to 0.949 for proposed NLMR models. As a result, both the LMR and NLMR models deliver almost the same predictive performance in estimating the Q for various weathering zones. Nevertheless, in most of the cases, NLMR models can provide higher performance prediction in estimating Q compared to LMR models.
ResearchGate has not been able to resolve any references for this publication.