Prediction of blast-induced ground vibration using artificial neural networks

Monjezi, Masoud; Ghafurikalajahi, M.; Bahrami, Amir

doi:10.1016/j.tust.2010.05.002

Cited by 194 publications

(86 citation statements)

References 14 publications

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“…In addition, burden, spacing, and burden-to-spacing ratio have been extensively utilized for predicting PPV by some researchers (Ghasemi et al 2013;Jahed Armaghani et al 2014;Ghoraba et al 2015;Hajihassani et al 2015a, b) in their predictive models. Apart from that, powder factor, stemming, and hole depth were set as input parameters in various studies (Monjezi et al 2011;Jahed Armaghani et al 2014;Hajihassani et al 2015a). Hence, in this research, burden-to-spacing ratio, stemming length, powder factor, the maximum charge per delay, hole depth, and distance from the blast face were selected and set as input parameters to predict PPV.…”

Section: Case Study and Input Selectionmentioning

confidence: 99%

“…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. They used a database comprising of 182 datasets in order to predict PPV and concluded that ANN can implement better in predicting PPV compared to other proposed models.…”

Section: Introductionmentioning

confidence: 99%

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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.

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146

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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: Case Study and Input Selectionmentioning

confidence: 99%

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.

Self Cite

146

View full text Add to dashboard Cite

show abstract

“…In another study, Monjezi et al [26] developed the ANN, empirical and statistical-based models for predicting blastinduced ground vibrations in Siahbisheh pumped storage dam, Iran. They used a database comprising 182 datasets to predict PPV and concluded that ANN can implement better in predicting PPV compared to other proposed models.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification and regression tree technique in estimating peak particle velocity caused by blasting

Khandelwal

Armaghani

Faradonbeh

et al. 2016

Engineering with Computers

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“…Wherever back break has been the blasting problem in a new bench, the lower amount of back break is considered as the blasting pattern evaluation factor Gate et al 2005;Khandelwal and Monjezi 2012;Monjezi et al 2012;Monjezi and Dehghani 2008;Monjezi et al 2010b). In addition, a few endeavours have been made to diminish ground vibration (Erarslan et al 2008;Ak et al 2009;Hudaverdi 2012;Shuran and Shujin 2011;Bakhshandeh Amnieh et al 2012;Dehghani and Ataee-Pour 2011;Monjezi et al 2010a;Guosheng et al 2011;Ak and Konuk 2008;Iphar et al 2008;Monjezi et al 2011b). The essential issue of these examinations is to recognize stand out of the impact criteria in the blasting operation enhancement.…”

Section: Introductionmentioning

confidence: 99%