Exploitation
Mohammad Reza Rezaei; Majid Noorian-Bidgoli
Abstract
Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling ...
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Drilling and blasting are crucial operations in open-pit mining, aimed at optimizing rock fragmentation, minimizing negative effects like backbreak and flyrock, and reducing costs, while enhancing efficiency and minimizing environmental and infrastructure impacts. This study focuses on optimizing drilling and blasting patterns at the Miduk copper mine using Multi-Criteria Decision-Making (MCDM) methods. The primary objectives were to achieve optimal fragmentation, minimize specific charge and drilling costs, and reduce undesirable phenomena like backbreak and flyrock caused by blasting. A total of 52 blasting patterns implemented at the mine were evaluated using various MCDM techniques, including TOPSIS, ELECTRE, VIKOR, and COCOSO. By constructing decision matrices and ranking the alternatives in each method, the most suitable blasting pattern was identified. The Copeland method was further applied to integrate the results from the decision models and establish a consensus on the final ranking of blasting patterns based on the criteria. The study's innovation lies in the application of advanced MCDM techniques to optimize drilling and blasting patterns, as well as the integration of results to enhance the decision-making process's accuracy. The optimal blasting pattern (M_Patt_03) was found to feature a burden of 6.5 meters, a spacing of 8 meters, and a borehole diameter of 150 millimetres, offering the best balance of fragmentation, charge efficiency, and drilling costs, while minimizing backbreak and flyrock. This study demonstrates the effectiveness of MCDM methods in optimizing complex engineering challenges in surface mining, providing a comprehensive framework for evaluating multiple criteria simultaneously and enabling more informed and balanced decision-making.
Exploration
Shirin Jahanmiri; Ali Aalianvari; Majid Noorian-Bidgoli; Jamal Rostami
Abstract
Accurate prediction of rock brittleness is essential for tunneling, excavation, and rock engineering applications. This study develops and compares four intelligent predictive models, namely Gene Expression Programming (GEP), Artificial Rabbit Optimization–GEP (ARO-GEP), Grey Wolf Optimizer–GEP ...
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Accurate prediction of rock brittleness is essential for tunneling, excavation, and rock engineering applications. This study develops and compares four intelligent predictive models, namely Gene Expression Programming (GEP), Artificial Rabbit Optimization–GEP (ARO-GEP), Grey Wolf Optimizer–GEP (GWO-GEP), and Crayfish Optimization Algorithm–GEP (COA-GEP), for estimating the rock brittleness index using uniaxial compressive strength (UCS) and tensile strength (TS). A comprehensive database containing 854 samples from 23 rock types was compiled from published literature. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Model performance was evaluated using R², RMSE, MAE, and MAPE metrics. The results demonstrate that all models successfully captured the nonlinear relationship between UCS, TS, and rock brittleness. Among the investigated models, GEP achieved the highest predictive accuracy with a testing R² of 0.95 and RMSE of 1.24, followed closely by ARO-GEP and GWO-GEP. Sensitivity and SHAP analyses confirmed that UCS is the dominant parameter controlling rock brittleness, contributing approximately 60% of the predictive influence. The developed models provide practical tools for preliminary brittleness estimation in situations where direct laboratory determination is unavailable
