Evaluation of blast-induced ground vibrations in open-pit mines by using adaptive neuro-fuzzy inference systems

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• 作者：Arzu Koçaslan ; A. Gürkan Yüksek ; Kazım Görgülü ; Ercan Arpaz
• 关键词：Blasting ; Peak particle velocity ; Adaptive neuro ; fuzzy inference system (ANFIS)
• 刊名：Environmental Earth Sciences
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：1
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Geology; Hydrology/Water Resources; Geochemistry; Environmental Science and Engineering; Terrestrial Pollution; Biogeosciences;
• 出版者：Springer Berlin Heidelberg
• ISSN：1866-6299
• 卷排序：76

文摘

This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tülü open-pit boron mine, the Kırka open-pit boron mine, and the TKI Çan coal mine fields. Distance (m, R) and maximum charge per delay (kg, W), stemming (m, SB), burden (m, B), and S-wave velocities (m/s, Vs) obtained from in situ field measurements have been chosen as input parameters for the adaptive neuro-fuzzy inference system (ANFIS)-based model in order to predict the peak particle velocity values. In the ANFIS model, 521 blasting data sets obtained from four fields have been used (r2 = 0.57–0.81). The coefficient of ANFIS model is higher than those of the empirical equation (r2 = 1). These results show that the ANFIS model to predict PPV values has a considerable advantage when compared with the other prediction models.