Exploitation
Ali Rezaei; Ebrahim Ghasemi; Ali Farhadian; Sina Ghavami
Abstract
In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified ...
Read More
In this study, a comprehensive investigation has been done on 10 different types of granite building stones from various mines in Iran. The study aims to investigate the relationship between the texture coefficient (TC) and abrasivity properties of the studied stones. Abrasivity of stones was quantified through six indices, including equivalent quartz content (EQC), rock abrasivity index (RAI), Schimazek abrasivity factor (F), Cerchar abrasivity index (CAI), building stone abrasivity index (BSAI), and the Taber wear index (Iw). Bi-variate regression analysis was applied to develop the predictive equations for relationship between TC and abrasivity indices. The investigations demonstrated that there is a direct relationship between TC and all abrasivity indices. Furthermore, TC has moderate to high relationship with abrasivity indices. After developing the equations, their accuracy was evaluated by performance criteria including determination coefficient (R2), the normalized root mean square error (NRMSE), the variance account for (VAF), and the performance index (PI). The strongest relationship was found between TC and RAI (with R2, VAF, NRMSE, and PI value of 0.850, 0.074, 85.386, and 1.630, respectively), while the weakest relationship was observed between TC and F (with R2, NRMSE, VAF, and PI value of 0.491, 0.532, 47.605, and 0.435, respectively). This research demonstrates importance of the textural characteristics of stones, especially TC as a reliable index, on the abrasivity properties of granite building stones. Thus, the equations developed herein can be practically used for estimating the stone abrasivity in building stone quarrying and processing projects.
Rock Mechanics
M. H. Kadkhodaei; E. Ghasemi
Abstract
The CERCHAR abrasivity test is very popular for determination of rock abrasivity. An accurate estimation of the CERCHAR abrasivity index (CAI) is useful for excavation operation costs. This paper presents a model to calculate CAI based on the gene expression programming (GEP) approach. This model is ...
Read More
The CERCHAR abrasivity test is very popular for determination of rock abrasivity. An accurate estimation of the CERCHAR abrasivity index (CAI) is useful for excavation operation costs. This paper presents a model to calculate CAI based on the gene expression programming (GEP) approach. This model is trained and tested based on a database collected from the experimental results available in the literature. The proposed GEP model predicts CAI based on two basic geomechanical properties of rocks, i.e. rock abrasivity index (RAI) and Brazilian tensile strength (BTS). Root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2) are used to measure the model performance. Furthermore, the developed GEP model is compared with linear and non-linear multiple regression and other existing models in the literature. The results obtained show that GEP is a strong technique for the prediction of CAI.
Exploitation
E. Ghasemi
Abstract
In underground excavation, where the road-headers are employed, a precise prediction of the road-header performance has a vital role in the economy of the project. In this paper, a new model is developed for prediction of the road-header performance using the non-linear multivariate regression analysis. ...
Read More
In underground excavation, where the road-headers are employed, a precise prediction of the road-header performance has a vital role in the economy of the project. In this paper, a new model is developed for prediction of the road-header performance using the non-linear multivariate regression analysis. This model is able to estimate the instantaneous cutting rate (ICR) of roadheader based on rock properties such as Brazilian tensile strength (BTS), rock mass cuttability index (RMCI), and alpha angle (α: is the angle between the tunnel axis and the planes of weakness). In order to construct and test the proposed model, a database including 62 cutting cases is used in the Tabas coal mine No. 1 in Iran. Various statistical performance indices were employed to evaluate the model efficiency. The results obtained indicate that the proposed non-linear regression model can be efficiently used to predict the road-header cutting performance. Furthermore, the prediction capacity of this model is better than the empirical models developed previously. Finally, it should be noted that the developed model is site-specific, and it can be used for preliminary estimation of ICR in future phases of Tabas coal mine No. 1. The outcome of this model can be helpful in adjustment of time-scheduling of the project.
