Seyed A. Mousavi; K. Ahangari; K. Goshtasbi
Abstract
Blast and stress release create cracks, fractures, and excavation damage zone in the remaining rock mass. Bench health monitoring (BHM) is crucial regarding bench health and safety in blast dynamic loading. Several empirical criteria have been proposed for a quick estimation of different parameters of ...
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Blast and stress release create cracks, fractures, and excavation damage zone in the remaining rock mass. Bench health monitoring (BHM) is crucial regarding bench health and safety in blast dynamic loading. Several empirical criteria have been proposed for a quick estimation of different parameters of a rock mass in the zone damaged by the blast. This work estimates the rock mass properties behind the blast hole based on the generalized Hoek-Brown failure criterion and quantitative disturbance factor (D). Considering a constant D value, either zero or one, for the entire rock mass, remarkably alters its strength and stability, resulting in very optimistic or very conservative analyses. Therefore, D is considered based on the elastic damage theory, and numerical simulation is conducted based on the finite difference software FLAC to investigate the vibration and damage threshold by monitoring the peak particle velocity (PPV) in the bench domain with different geometries. According to the numerical simulation, as the depth behind the blast hole increases, the value of D decreases from one to zero almost non-linearly, resulting in a non-linear reduction in the Hoek-Brown behavioral model properties. It is found that using various parameters of rock mass in the blast-induced damage zone behind the hole leads to thoroughly different PPV values than the constant parameters. Accordingly, the approach to using the quantified values of parameter D is of great importance in the estimation of various properties of a rock mass in the blast-induced zone, as well as calculation of the vibration.
A. Srivastava; B. Singh Choudhary; M. Sharma
Abstract
Blast-induced ground vibration (PPV) evaluation for a safe blasting is a long-established criterion used mainly by the empirical equations. However, the empirical equations are again considering a limited information. Therefore, using Machine Learning (ML) tools [Support Vector Machine (SVM) and Random ...
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Blast-induced ground vibration (PPV) evaluation for a safe blasting is a long-established criterion used mainly by the empirical equations. However, the empirical equations are again considering a limited information. Therefore, using Machine Learning (ML) tools [Support Vector Machine (SVM) and Random Forest (RF)] can help in this context, and the same is applied in this work. A total of 73 blasts are monitored and recorded in this work. For the ML tools, the dataset is divided into the 80-20 ratio for the training and testing purposes in order to evaluate the performance capacity of the models. The prediction accuracies by the SVM and RF models in predicting the PPV values are satisfactory (up to 9% accuracy). The results obtained show that the coefficient of determination (R2) for RF and SVM is 0.81 and 0.75, respectively. Compared to the existing linear regressions, this work recommends using a machine learning regression model for the PPV prediction.
A. Siamaki; H. Bakhshandeh Amnieh
Abstract
A considerable amount of energy is released in the form of shock wave from explosive charge detonation. Shock wave energy is responsible for the creation of crushing and fracture zone around the blast hole. The rest of the shock wave energy is transferred to rock mass as ground vibration. Ground vibration ...
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A considerable amount of energy is released in the form of shock wave from explosive charge detonation. Shock wave energy is responsible for the creation of crushing and fracture zone around the blast hole. The rest of the shock wave energy is transferred to rock mass as ground vibration. Ground vibration is conveyed to the adjacent structures by body and surface waves. Geological structures like faults, fractures, and fillings play important roles in the wave attenuation. Studying the mechanism of ground wave propagation from blasts gives a better understanding about the stress wave transmission and its effect on the near structures. In this research work, the stress wave transmissions from discontinuities and fillings were evaluated using a field measurement and a Universal Distinct Element Code (UDEC). A single-hole blast was conducted in the Kangir dam, and the resulting vibrations were measured in many points before and after the faults. Numerical simulation shows the effects of geo-mechanical properties of fillings on the reflection and refraction rate of the stress wave. There are more energy reflections in the rock boundaries and soil fillings, and more energy is absorbed by soil fillings compared with rock fillings. Furthermore, there is a close correlation between the ground vibration records for the Kangir dam and the numerical results. The maximum relative error between the actual records and the simulated ones was found to be 18.5%, which shows the UDEC ability for the prediction of blast vibrations.
Hassan Bakhsandeh Amnieh; Alireza Mohammadi; M Mozdianfard
Abstract
Ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. Controlling vibration caused by blasting ...
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Ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. Controlling vibration caused by blasting can be achieved once peak particle velocity (PPV) is predicted. In this paper, the values of PPV have been predicted and compared using the artificial neural network (ANN), multivariate regression analysis (MVRA) and empirical relations. The necessary data were gathered from 11 blast operations in Sarcheshmeh copper mine, Kerman. The neural network input parameters include distance from blast point, maximum charge weight per delay, spacing, stemming and the number of drill-hole rows in each blasting operation. The network is of the multi-layer perception (MLP) type with 24 sets of training data including 2 hidden layers, 1 output layer with the network architecture of {5-11-12-1}, and Sigmoid tangent and linear transfer functions. To insure the training accuracy, the network was tested by 6 data sets; the determination coefficient and the average relative error were found to be 0.977 and 8.85%, respectively, showing the MLP network’s high capability and precision in estimating the values of the PPV. To predict these values, MVRA and empirical relations were analyzed. The results have revealed that these relations have low capability in estimating the PPV parameter.
