Rock Mechanics
Alireza Afradi; Arash Ebrahimabadi; Mansour Hedayatzadeh
Abstract
Tunnel Boring Machines (TBMs) are extensively used to excavate underground spaces in civil and tunneling projects. An accurate evaluation of their penetration rate is the key factor for the TBM performance prediction. In this study, artificial intelligence methods are used to predict the TBM penetration ...
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Tunnel Boring Machines (TBMs) are extensively used to excavate underground spaces in civil and tunneling projects. An accurate evaluation of their penetration rate is the key factor for the TBM performance prediction. In this study, artificial intelligence methods are used to predict the TBM penetration rate in excavation operations in the Kerman tunnel and the Gavoshan water conveyance tunnels. The aim of this paper is to show the application of the Multivariate Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) for the TBM penetration rate prediction. The penetration rate parameter is considered as a dependent variable, and the Rock Quality Designation (RQD), Brazilian Tensile Strength (BTS), Uniaxial Compressive Strength (UCS), Density (D), Joint Angle (JA), Joint Spacing (JS), and Poisson's Ratio are considered as independent variables. The obtained results by the several proposed methods indicated a high accuracy between the predicted and measured penetration rates, but the support vector machine yields more precise and realistic outcomes.
Iraj Alavi; Arash Ebrahimabadi; Hadi Hamidian
Abstract
Estimating the costs of mine reclamation is a significant part of mine closure projects. One approach to mine reclamation is planting mine areas. In this approach, the optimum selection of plant types is cosidered a multiple-criteria decision-making (MCDM) problem. Once proper plant species are identified, ...
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Estimating the costs of mine reclamation is a significant part of mine closure projects. One approach to mine reclamation is planting mine areas. In this approach, the optimum selection of plant types is cosidered a multiple-criteria decision-making (MCDM) problem. Once proper plant species are identified, it is required to estminate planting costs through statistical analysis. This work aims to introduce an algorithm for optimal plant type selection and a reclamation cost estimation model for open-pit mines. To this end, the plant species compatible with the sorrounding areas of Sungun copper mine are identified and ranked using the PROMETHEE technique. In this analysis, the main criteria are local landscape, pest resistance, plant growth ability, availability, economic issues, soil protection, water storage ability, and pollution prevention. Among the six plant types, Maple trees have the highest score (4.34). After that, to develop the reclamation cost estimation model, the data (99 datasets) is collected from the Sungun copper mine, Sarcheshmeh copper mine, and Chadormaloo iron mine. The variables in the database include soil gradation by graders, slope trimming and topography by bulldozers, the ripping and softening of the compacted soil, chemical fertilizers, natural fertilizers and mulch and biosolid, lime soil pH adjustment, herbicide, seedling, tree planting, workers and drivers, and fuel and maintenance. Regression analysis is performed to analyze the data, and a reclamation cost estimation model is developed with high accuracy (R2 = 0.78). On the whole, this study proposes an innovative, step-by-step, technical, and economic approach to the optimal selection of plant species, and presents a reclamation cost estimation model so as to promote the open-pit mine reclamation process.
E. Pouresmaeili; A. Ebrahimabadi; H. Hamidian
Abstract
Sustainability assessment has received numerous attentions in the mining industry. Mining sustainability includes the environmental, economic, and social dimensions, and a sustainable development is achieved when all these dimensions improve in a balanced manner. Therefore, to measure the sustainability ...
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Sustainability assessment has received numerous attentions in the mining industry. Mining sustainability includes the environmental, economic, and social dimensions, and a sustainable development is achieved when all these dimensions improve in a balanced manner. Therefore, to measure the sustainability score of a mine, we require an approach that evaluates all these three dimensions of mining sustainability. Some frameworks have been developed to compute the sustainability score of mining activities; however, some of them are very complicated and the others do not cover all the environmental, economic, and social aspects of sustainability. In order to fill this gap, this work was designed to introduce a practical approach to determine the score of mining sustainability. In order to develop this approach, initially, 14 negative and positive influential macro factors in the sustainability of open-pit mines were identified. Then the important levels of the factors were estimated based on the comments and scores of some experts. Two checklists were constructed for the negative and positive factors. The sustainability score was computed using these checklists and the importance levels of the factors. The score range was between -100 and +100. In order to implement the proposed approach, the Angouran lead and zinc mine was selected. The sustainability score of the Angouran mine was +47.91, which indicated that the this mine had a sustainable condition. This score could increase through modification of some factors.
