M. Zahiri; K. Goshtasbi; J. Khademi Hamidi; K. Ahangari
Abstract
There is a direct relationship between the efficiency of mechanized excavation in hard rocks and that of disc cutters. Disc cutter wear is an important effective factor involved in the functionality of tunnel boring machines. Replacement of disc cutters is a time-consuming and costly activity that can ...
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There is a direct relationship between the efficiency of mechanized excavation in hard rocks and that of disc cutters. Disc cutter wear is an important effective factor involved in the functionality of tunnel boring machines. Replacement of disc cutters is a time-consuming and costly activity that can significantly reduce the TBM utilization and advance rate, and has a major effect on the total time and cost of the tunneling projects. When these machines bore through hard rocks, the cutter wear considerably affects the excavation process. To evaluate the performance of the cutters, first, it is essential to figure out how they operate the rock cutting mechanism; secondly, it is important to identify the key factors that cause the wear. In this work, we attempt to introduce a comprehensive numerical method for estimation of disc cutter wear. The field data including the actual cutter wear more than 1000 pieces and the geological parameters along the Kani-Sib transmission tunnel in the northwest of Iran are compiled in a special database that is subjected to a statistical analysis in order to reveal the genuine wear rule. The results obtained from the numerical method indicate that with an increase in the wear of disk cutter up to 25 mm, the applied normal and rolling forces can be multiplied by 2.9 and 2.7, respectively, and by passing the critical wear, the disk cutters lose their optimal performance. This method also shows that confining pressure will increase the wear of the disc cutter. By the proposed formulation, the cutter consumption rate can be predicted with a high accuracy.
Mine Economic and Management
R. Bastami; A. Aghajani Bazzazi; H. Hamidian Shoormasti; K. Ahangari
Abstract
The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone ...
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The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (GEP), linear multivariate regression (LMR), and non-linear multivariate regression (NLMR) models. In all models, the ANFO value, number of detonators, Emolite value, hole number, hole length, hole diameter, burden, spacing, stemming, sub-drilling, specific gravity of rock, hardness, and uniaxial compressive strength are used as the input parameters. The ANN model results in the test stage indicating a higher correlation coefficient (0.954) and a lower root mean square error (973) compared to the other models. In addition, it has a better conformity with the real blasting costs in comparison with the other models. Although the ANNs method is regarded as one of the intelligent and powerful techniques in parameter prediction, its most important fault is its inability to provide mathematical equations for engineering operations. In contrast, the GEP model exhibits a reliable output by presenting a mathematical equation for BC prediction with a correlation coefficient of 0.933 and a root mean square error of 1088. Based on the sensitivity analysis, the spacing and ANFO values have the maximum and minimum effects on the BC function, respectively. The number of detonators, Emolite value, hole number, specific gravity, hardness, and rock uniaxial compressive strength have a positive correlation with BC, while the ANFO value, hole length, hole diameter, burden, spacing, stemming, and sub-drilling have a negative correlation with BC.