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

Kocaslan A., YÜKSEK A. G., GÖRGÜLÜ K., ARPAZ E.

ENVIRONMENTAL EARTH SCIENCES, cilt.76, sa.1, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 76 Sayı: 1
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1007/s12665-016-6306-x
  • Dergi Adı: ENVIRONMENTAL EARTH SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Blasting, Peak particle velocity, Adaptive, neuro-fuzzy, inference system ( ANFIS), SARCHESHMEH COPPER MINE, PREDICTION, NETWORK, FREQUENCY, QUARRY
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

This study addresses the effects of rock characteristics and blasting design parameters on blast-induced vibrations in the Kangal open-pit coal mine, the Tulu openpit boron mine, the Kirka open-pit boron mine, and the TKI C, an 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 (r (2) = 0.57-0.81). The coefficient of ANFIS model is higher than those of the empirical equation (r (2) = 1). These results show that the ANFIS model to predict PPV values has a considerable advantage when compared with the other prediction models.