Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach

Khandelwal, Manoj; Singh, T. N.

doi:10.1016/j.jsv.2005.02.044

Cited by 253 publications

(100 citation statements)

References 5 publications

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“…Many scholars highlight the successful use of these techniques in the field of ground vibration prediction. Khandelwal and Singh (2006) examined empirical predictors and artificial neural network (ANN) model to predict PPV and frequency values obtained from 150 blasting events and concluded that ANN results are more accurate compared to empirical predictors. In another study of ground vibration prediction, Monjezi et al (2011) developed ANN, empirical and statistical models for blasting operations conducted in Siahbisheh pumped storage dam, Iran.…”

Section: Introductionmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

149

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Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

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Section: Introductionmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

149

View full text Add to dashboard Cite

show abstract

“…The explosives release a large amount of energy during explosion, the majority of which is wasted in the form of ground vibration, backbreaks, air breaks, flying rocks and noises. Only 25-30% of the explosive energy is utilized for breaking and displacement of rocks (Khandelwal et al 2005, Khandelwal andSingh 2006). Energy generated during blasting has not been fully utilized for rock breakage due to the presence of uncertainties in rock parameters.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of total charge and maximum charge per delay on ground vibration

Singh¹,

Verma

2010

Geomatics, Natural Hazards and Risk

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Frequency is among the most used properties for determining the detrimental effect of ground blasting on the surrounding areas. In this paper, an attempt has been made to calculate the frequency of the blast by developing an adaptive neuro-fuzzy inference system (ANFIS) model. Blast design and explosive parameters are incorporated in the intelligent model. The sugeno type fuzzy inference system was generated using the subtractive clustering partition method. Two separate ANFIS networks have been generated, each with five inputs (maximum charge per delay and total charge, respectively in two different cases) and one output parameter (frequency) was trained using a hybrid parameter optimization method. A set of 160 data obtained from blast monitoring at a major surface coal mine in India was used for training the network. A different set of 27 data was used for validating and testing the designed network. It has been observed that for a given distance the frequency of the vibration is affected not only by the maximum charge per delay but by total charge also. The results obtained from ANFIS were able to predict that the frequency of ground blast operation is obtained with higher precision when the maximum charge per delay is used as an input parameter instead of total charge.

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“…It is strongly suggested not to set the error goal at absolute zero; rather it should be slightly higher than zero as Refs. [40, 125,126] fixed the error goal at 0.001, 0.01 and 0.005 respectively. This is because vibration response usually contains noises to some degrees due to poor sensor calibration, high sensitivity or operational fault of sensors, structure's non-uniformity in material, etc.…”

Section: Error Goalmentioning

confidence: 99%

Artificial neural networks for vibration based inverse parametric identifications: A review

Hossain

Ong

Ismail

et al. 2017

Applied Soft Computing

103

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Graphical abstract2 Highlights  ANNs-solved vibration based parametric identification studies are reviewed. Factors which affect identification result are discussed. Pros and cons of ANN approaches are mentioned. 1• Fundamentals of ANNs. 2• Dynamic behavior analysis for parametric identifications.• Reason for adopting ANNs to vibrational inverse identifications. 3• Earlier ANN approaches to different vibrational parametric identifications.--Signal pre-processing techniques --Input-output schemes --ANN models 4• Factors that affect ANNs performance for vibrational paramteric identification. 5• Advantages and disadvantages of ANN approaches. 6• Suggestions to potential researchers.• Experimental validation of authors' suggestion based on the literature.3  Suggestions are given to potential researchers based on the discussion. Analysis with experimental results is provided to justify some point of view. AbstractVibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs' prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods. Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes.

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Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach

Cited by 253 publications

References 5 publications

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Sensitivity of total charge and maximum charge per delay on ground vibration

Artificial neural networks for vibration based inverse parametric identifications: A review

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