Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS

Trivedi, Ratnesh; Singh, T. N.; Gupta, Neel

doi:10.1007/s10706-015-9869-5

Cited by 88 publications

(21 citation statements)

References 29 publications

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“…The space in tunnel is modeled by the hexahedron grid unit, which is defined as the Euler hexahedron filled with ideal air. The explosive source is applied as an ideal spherical TNT explosive defined by the Jones-Wilkins-Leeequation of state equation [7,8]. ALE method is used to define the interface between the Lagrange and Euler units.…”

Section: Composite Shell Simulation Modelingmentioning

confidence: 99%

Response Analysis of Composite Shell with Various Fixation Patterns under Axial Impact Load in Tunnel

Song¹,

Wei²

2017

dteees

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Abstract. The safety equipment will against the explosive load when a disaster occurs in tunnel. This analysis of composite shell establishes a model of the relation among initial velocity, explosive load, plastic strain and shell fixation pattern, which is used to study the plastic strain of composite shell under different fixation patterns. The result shows that the fixation pattern plays a great part in the plastic strain of composite shells under axial impact load in tunnel. It is clearly that the extreme value and distribution range of plastic strain can be controlled by changing the fixation pattern. If the fixed point of composite shell is axisymmetric, the extreme value of plastic strain will decrease. Meanwhile, the distribution ranges of it remaining the same level. It is also confirmed that the plastic strain of fixed area which is far away from the explosion is more sensitive than the closer one to impact load.

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Section: Composite Shell Simulation Modelingmentioning

confidence: 99%

Response Analysis of Composite Shell with Various Fixation Patterns under Axial Impact Load in Tunnel

Song¹,

Wei²

2017

dteees

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“…Monte Carlo (MC) simulation, which is known as probability simulation, is a computerized technique to realize the effect of risk and uncertainty in diverse forecasting models for various issues such as project management, financial, cost, and decision-making problems. MC simulation uses a repeated random sampling method with the help of statistical analysis to achieve a probabilistic approximation for an arithmetic equation or developed model (US EPA Technical Panel 1997;Solver 2010). When a predictive model (every model that creates results for future) is developed, there is a need to consider a series of assumptions related to the problem and estimate the expected value(s).…”

Section: Evaluation Of the Developed Mr Modelmentioning

confidence: 99%

“…Sometimes, the range of actual data is unknown, so, considering the historical data in a specific field, a suitable estimation can be drawn. However, the developed predictive model can be used for estimation; it contains some inherent uncertainties and risks (Solver 2010).…”

Section: Evaluation Of the Developed Mr Modelmentioning

confidence: 99%

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Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

et al. 2016

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Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

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“…However, not 100% of explosives energy was used to break rock [3]. According to previous scientists, up to 80-85% of the energy of explosives was wasted and produced ill effects, such as ground vibration (PPV), air overpressure, fly rock, dust and toxic [4][5][6][7][8]. Of these side effects, ground vibration is the most dangerous effect [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Nguyen

2019

SN Appl. Sci.

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Blast-induced ground vibration (PPV) is one of the effects of the hazard in open-pit mines. It can make vibration of the structure, instability of slope and bench, impact on underground water, and the surrounding residential. Therefore, the precise prediction of blast-induced PPV is needed to minimize undesirable effects on the surrounding environment. Also, one of the most important objectives of this study is to determine the site characteristics for the application of controlled blasting techniques. In this study, the support vector regression (SVR) approach was considered and developed for predicting blast-induced PPV in an open-pit coal mine (Vietnam) as a case study. Three forms of the equation include linear (L), polynomial (P), and radius basis function (RBF) were applied to develop the SVR models. For comparison purpose, an empirical technique was also referred to as estimate blast-induced PPV in this study on the same training dataset. A database with 181 blasting events was used for this aim. Performance indicators such as root-mean-square error (RMSE) and the coefficient of determination (R 2 ) were used to compare and evaluate the performance of the predictive models. The results showed that SVR was an effective approach for predicting blast-induced PPV in this study. Among three forms of the equation, the SVR model with RBF was the most superior model for predicting blast-induced PPV in this study with an RMSE of 0.396, R 2 of 0.924, and MAE of 0.135, whereas the empirical model only obtained performance with an RMSE of 0.856, R 2 of 0.643, and MAE of 0.575. This study provided an overview of the SVR approach in predicting blast-induced PPV. A comparison of different kernel functions for the selection of the SVR model is needed to find out the best model for predicting blast-induced PPV.

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Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS

Cited by 88 publications

References 29 publications

Response Analysis of Composite Shell with Various Fixation Patterns under Axial Impact Load in Tunnel

Response Analysis of Composite Shell with Various Fixation Patterns under Axial Impact Load in Tunnel

Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

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