Prediction of Vibration Velocity Generated in Mine Blasting Using Support Vector Regression Improved by Optimization Algorithms

Natural Resources Research - Ground vibration generated from blasting is a detrimental side effect of the use of explosives to break the rock mass in mines. Therefore, accurately predicting ground...     

A Risk-Based Technique to Analyze Flyrock Results Through Rock Engineering System

Flyrock is an adverse effect produced by blasting in open-pit mines and tunneling projects. So, it seems that the precise estimations and risk level assessment of flyrock are essential in minimizing environmental effects induced by blasting. The first aim of this research is to model the risk level associated with flyrock through rock engineering systems (RES) methodology. In this regard, 62 blasting were investigated in Ulu Tiram quarry, Malaysia, and the most effective parameters of flyrock were measured. Using the most influential parameters on flyrock, the overall risk of flyrock was obtained as 32.95 which is considered as low to medium degree of vulnerability. Moreover, the second aim of this research is to estimate flyrock based on RES and multiple linear regression (MLR). To evaluate performance prediction of the models, some statistical criteria such as coefficient of determination (R²) were computed. Comparing the values predicted by the models demonstrated that the RES has more suitable performance than MLR for predicting the flyrock and it could be introduced as a powerful technique in this field.     

A Fuzzy Rule-Based Approach to Address Uncertainty in Risk Assessment and Prediction of Blast-Induced Flyrock in a Quarry

Strict control of the environmental impacts of blasting operations needs to be completely in line with the regulatory limits. In such operations, flyrock control is of high importance especially due to safety issues and the damages it may cause to infrastructures, properties as well as the people who live within and around the blasting site. Such control causes flyrock to be limited, hence significantly reducing the risk of damage. This paper serves two main objectives: risk assessment and prediction of flyrock. For these objectives, a fuzzy rock engineering system (FRES) framework was developed in this study. The proposed FRES was able to efficiently evaluate the parameters that affect flyrock, which facilitate decisions to be made under uncertainties. In this study, the risk level of flyrock was determined using 11 independent parameters, and the proposed FRES was capable of calculating the interactions among these parameters. According to the results, the overall risk of flyrock in the studied case (Ulu Tiram quarry, located in Malaysia) was medium to high. Hence, the use of controlled blasting method can be recommended in the site. In the next step, three optimization algorithms, namely genetic algorithm (GA), imperialist competitive algorithm (ICA) and particle swarm optimization (PSO), were used to predict flyrock, and it was found that the GA-based model was more accurate than the ICA- and PSO-based models. Accordingly, it is concluded that FRES is a very useful for both risk assessment and prediction of flyrock.

     

Prediction of blasting-induced fragmentation in Meydook copper mine using empirical,statistical, and mutual information models

One of the most important aims of blasting in open pit mines is to reach desirable size of fragmentation. Prediction of fragmentation has great importance in an attempt to prevent economic drawbacks. In this study, blasting data from Meydook mine were used to study the effect of different parameters on fragmentation; 30 blast cycles performed in Meydook mine were selected to predict fragmentation where six more blast cycles are used to validate the results of developed models. In this research, mutual information (MI) method was employed to predict fragmentation. Ten parameters were considered as primary ones in the model. For the sake of comparison, Kuz-Ram empirical model and statistical modeling were also used. Coefficient of determination (R ²), root mean square error (RMSE), and mean absolute error (MAE) were then used to compare the models. Results show that MI model with values of R ², RMSE, and MAE equals 0.81, 10.71, and 9.02, respectively, is found to have more accuracy with better performance comparing to Kuz-Ram and statistical models.     

Study of blasting vibrations in Sarcheshmeh copper mine

Introduction In spite of development of mechanized methods of ground excavation, drilling and blasting is still extensively employed because of its low capital investment and simplicity. Its extensive use is not even limited by extension of mines close to residential areas and vital establishments. If it is not used in a controlled way, blasting operation can cause instability, failure of mine slopes and damps and damage to the nearby structures. The main objective here could be to reduce the …     

ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting

Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.

     

伊朗Sarcheshmeh铜矿爆破振动研究di

地面振动是爆破产生的副效应之一，爆破中，相当一部分能量通过其产生的地面振动而被消耗，从而导致矿山产量大降，甚至影响矿山的发展．本文处理并分析了在伊朗Kerman 省Sarcheshmeh铜矿11次爆破记录到的57个三分向地震图．这些数据通过数字化地震仪PDAS-100记录得到，并使用DADISP软件加以分析．最后，重新考察了影响地面振动的爆破参数，比如炸药的重量和种类、结构物与爆破点的距离、爆破延时等，并研究了它们对峰值质点速度（vPP）的影响．在此基础上，确定了合适的爆破延时及炸药种类．通过研究这些数据，为Sarcheshmeh铜矿作出了vPP与换算距离之间的关系图．有了这样的关系图，就能够确定结构物的安全距离及相应的炸药数量．      

Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system

Ground vibration resulting from blasting is one of the most important environmental problems at open-cast mines. Therefore, accurately approximating the blast-induced ground vibration is very significant. By reviewing the previous investigations, many attempts have been done to create the empirical models for estimating ground vibration. Nevertheless, the performance of the empirical models is not good enough. In this research work, a new hybrid model of fuzzy system (FS) designed by imperialistic competitive algorithm (ICA) is proposed for approximating ground vibration resulting from blasting at Miduk copper mine, Iran. For comparison aims, various empirical models were also utilized. Results from different predictor models were compared by using coefficient of multiple determination (R ²), variance account for and root-mean-square error between measured and predicted values of the PPVs. Results prove that the FS–ICA model outperforms the other empirical models in terms of the prediction accuracy. In other words, the FS–ICA model with R ² of 0.942 can forecast PPV better than the USBM with R ² of 0.634, Ambraseys–Hendron with R ² of 0.638, Langefors–Kihlstrom with R ² of 0.637 and Indian Standard with R ² of 0.519.