Abstract
Brittleness is one of the most crucial rock features for underground excavation and design considerations in rock mass. Direct standard testing method for measuring rock brittleness, the combination of rock properties rather than only one rock parameter have not available yet. Therefore, it is indirectly calculated as a function of some rock properties such as rock strength by using various ratios and prediction tools. The aim of this study is to estimate the rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values.
| Original language | English |
|---|---|
| Pages (from-to) | 2265-2272 |
| Number of pages | 8 |
| Journal | Expert Systems with Applications |
| Volume | 37 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Mar 15 2010 |
| Externally published | Yes |
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Keywords
- Brittleness
- Fuzzy inference system
- Nonlinear regression
ASJC Scopus subject areas
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
Cite this
Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. / Yagiz, Saffet; Gokceoglu, Candan.
In: Expert Systems with Applications, Vol. 37, No. 3, 15.03.2010, p. 2265-2272.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness
AU - Yagiz, Saffet
AU - Gokceoglu, Candan
PY - 2010/3/15
Y1 - 2010/3/15
N2 - Brittleness is one of the most crucial rock features for underground excavation and design considerations in rock mass. Direct standard testing method for measuring rock brittleness, the combination of rock properties rather than only one rock parameter have not available yet. Therefore, it is indirectly calculated as a function of some rock properties such as rock strength by using various ratios and prediction tools. The aim of this study is to estimate the rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values.
AB - Brittleness is one of the most crucial rock features for underground excavation and design considerations in rock mass. Direct standard testing method for measuring rock brittleness, the combination of rock properties rather than only one rock parameter have not available yet. Therefore, it is indirectly calculated as a function of some rock properties such as rock strength by using various ratios and prediction tools. The aim of this study is to estimate the rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values.
KW - Brittleness
KW - Fuzzy inference system
KW - Nonlinear regression
UR - http://www.scopus.com/inward/record.url?scp=70449523078&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449523078&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2009.07.046
DO - 10.1016/j.eswa.2009.07.046
M3 - Article
VL - 37
SP - 2265
EP - 2272
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 3
ER -