Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction
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 b...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Center for Environmental and Energy Research and Studies
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/72505/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971602144&doi=10.1007%2fs13762-016-0979-2&partnerID=40&md5=e457bbeade8a09e8eb23e3402a80fc0e |
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| Summary: | 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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