Exploitation
Abbas Khajouei Sirjani; Farhang Sereshki; Mohammad Ataei; Mohammad Amiri Hossaini
Abstract
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for ...
Read More
The most significant detrimental consequence of blasting operations is ground vibration. This phenomenon not only causes instability in the mine walls but also extends its destructive effects to various facilities and structures over several kilometers. Various researchers have proposed equations for predicting Peak Particle Velocity (PPV), which are typically based on two parameters: the charge per delay and the distance to the blast site. However, according to different studies, the results of blasting operations are influenced by several factors, including the blast pattern, rock mass properties, and the type of explosives used. Since artificial intelligence technology has not yet been fully assessed in the mining industry, this study employs linear and nonlinear statistical models to estimate PPV at Golgohar Iron Ore Mine No. 1. To achieve this goal, 58 sets of blasting data were collected and analyzed, including parameters such as blast hole length, burden thickness, row spacing of the blast holes, stemming length, the number of blast holes, total explosive charge, the seismograph's distance from the blast site, and the PPV recorded by an explosive system using a detonating fuse. In the first stage, ground vibration was predicted using linear and nonlinear multivariate statistical models. In the second stage, to determine the objective function for optimizing the blast design using the shuffled frog-leaping algorithm, the performance of the statistical models was evaluated using R², RMSE, and MAPE indices. The multivariate linear statistical model, with R² = 0.9247, RMSE = 9.235, and MAPE = 12.525, was proposed and used as the objective function. Ultimately, the results showed that the combination of the statistical model technique with the shuffled frog-leaping algorithm could reduce PPV by up to 31%.
M. Ghaedi Ghalini; M. Bahaaddini; M. Amiri Hossaini
Abstract
Estimation of the in-situ block size is known as a key parameter in the characterization of the mechanical properties of rock masses. As the in-situ block size cannot be measured directly, several simplified methods have been developed, where the intrinsic variability of the geometrical features of discontinuities ...
Read More
Estimation of the in-situ block size is known as a key parameter in the characterization of the mechanical properties of rock masses. As the in-situ block size cannot be measured directly, several simplified methods have been developed, where the intrinsic variability of the geometrical features of discontinuities are commonly neglected. This work aims to estimate the in-situ block size distribution (IBSD) using the combined photogrammetry and discrete fracture network (DFN) approaches. To this end, four blasting benches in the Golgohar iron mine No. 1, Sirjan, Iran, are considered as the case studies of this research work. The slope faces are surveyed using the photogrammetry method. Then 3D images are prepared from the generated digital terrain models, and the geometrical characteristics of discontinuities are surveyed. The measured geometrical parameters are statistically analysed, and the joint intensity, the statistical distribution of the orientation, and the fracture trace length are determined. The DFN models are generated, and IBSD for each slope face is determined using the multi-dimensional spacing method. In order to evaluate the validity of the generated DFN models, the geological strength index (GSI) as well as the stereographic distribution of discontinuities in the DFN models are compared against the field measurements. A good agreement has been found between the results of the DFN models and the filed measurements. The results of this work show that the combined photogrammetry and DFN techniques provide a robust, safe, and time-efficient methodology for the estimation of IBSD.