Quantifying rock mass behavior around underground

A. C. Adoko, P. T. Phumaphi, T. Zvarivadza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A quantitative assessment of rock mass behaviors around underground excavations is essential in mining since it can assist engineers in selecting appropriate mining methods and implementing reliable ground control measures. In this paper, the interactions between the factors affecting the rock mass behaviors around underground excavations are quantified using the Rock Engineering Systems (RES) and Artificial Neural Network (ANN) approaches. To this end, the ground behavior index (GBI) is developed. The RES is applied as a practical tool for determining complex and highly nonlinear correlation among the input parameters via the interaction matrices while ANN is implemented to objectively assign weights to the input parameters of the GBI. Fall of ground (FoG) of rock mass surrounding the excavation comprising gravity induced structurally controlled, block movement and stress induced failure cases were investigated and a comprehensive database on the FoG characteristics was compiled. Several parameters related to the FoG including the rock mass characteristics, the excavation geometry, the excavation supports, the mining methods and the FoG size, were selected to establish the GBI. The Bamangwato Concession Limited (BCL), an underground mine located in Selibe-Phikwe, Botswana was used as case study to compute the proposed GBI. Overall, the validation results showed excellent agreement between the GBI and the field observations. It was concluded that the GBI could be used to provide engineers with reliable quantitative information on the fall of ground as well as the corresponding the hazard level.

Original languageEnglish
Title of host publication51st US Rock Mechanics / Geomechanics Symposium 2017
PublisherAmerican Rock Mechanics Association (ARMA)
Pages2759-2767
Number of pages9
Volume4
ISBN (Electronic)9781510857582
Publication statusPublished - Jan 1 2017
Event51st US Rock Mechanics / Geomechanics Symposium 2017 - San Francisco, United States
Duration: Jun 25 2017Jun 28 2017

Conference

Conference51st US Rock Mechanics / Geomechanics Symposium 2017
CountryUnited States
CitySan Francisco
Period6/25/176/28/17

Fingerprint

Excavation
excavation
Rocks
rocks
rock
Systems engineering
systems engineering
artificial neural network
engineers
Neural networks
Engineers
Botswana
engineering
ground control
Hazards
Gravitation
hazards
index
Geometry
hazard

ASJC Scopus subject areas

  • Geochemistry and Petrology
  • Geophysics

Cite this

Adoko, A. C., Phumaphi, P. T., & Zvarivadza, T. (2017). Quantifying rock mass behavior around underground. In 51st US Rock Mechanics / Geomechanics Symposium 2017 (Vol. 4, pp. 2759-2767). American Rock Mechanics Association (ARMA).

Quantifying rock mass behavior around underground. / Adoko, A. C.; Phumaphi, P. T.; Zvarivadza, T.

51st US Rock Mechanics / Geomechanics Symposium 2017. Vol. 4 American Rock Mechanics Association (ARMA), 2017. p. 2759-2767.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Adoko, AC, Phumaphi, PT & Zvarivadza, T 2017, Quantifying rock mass behavior around underground. in 51st US Rock Mechanics / Geomechanics Symposium 2017. vol. 4, American Rock Mechanics Association (ARMA), pp. 2759-2767, 51st US Rock Mechanics / Geomechanics Symposium 2017, San Francisco, United States, 6/25/17.
Adoko AC, Phumaphi PT, Zvarivadza T. Quantifying rock mass behavior around underground. In 51st US Rock Mechanics / Geomechanics Symposium 2017. Vol. 4. American Rock Mechanics Association (ARMA). 2017. p. 2759-2767
Adoko, A. C. ; Phumaphi, P. T. ; Zvarivadza, T. / Quantifying rock mass behavior around underground. 51st US Rock Mechanics / Geomechanics Symposium 2017. Vol. 4 American Rock Mechanics Association (ARMA), 2017. pp. 2759-2767
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