prediction of rock brittleness using nondestructive methods for hard rock tunneling

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ID: 181317
2016
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Abstract
The material and elastic properties of rocks are utilized for predicting and evaluating hard rock brittleness using artificial neural networks (ANN). Herein hard rock brittleness is defined using Yagiz' method. A predictive model is developed using a comprehensive database compiled from 30 years' worth of rock tests at the Earth Mechanics Institute (EMI), Colorado School of Mines. The model is sensitive to density, elastic properties, and P- and S-wave velocities. The results show that the model is a better predictor of rock brittleness than conventional destructive strength-test based models and multiple regression techniques. While the findings have direct implications on intact rock, the methodology can be extrapolated to rock mass problems in both tunneling and underground mining where rock brittleness is an important control.
Reference Key
kaunda2016journalprediction Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Rennie B. Kaunda;Brian Asbury
Journal journal of neurotrauma
Year 2016
DOI
10.1016/j.jrmge.2016.03.002
URL
http://www.sciencedirect.com/science/article/pii/S1674775516300038
Keywords
underground construction rock mechanics soil mechanics

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