Prediction the micro-Deval abrasion loss of rock aggregates from mainly the ultrasonic pulse velocity and some strength parameters

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GÜNEYLİ H., Güneyli A., YAPICI N., KARAHAN S.

ARABIAN JOURNAL OF GEOSCIENCES, cilt.6, sa.15, ss.527-536, 2022 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 6 Sayı: 15
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s12517-022-09717-9
  • Dergi Adı: ARABIAN JOURNAL OF GEOSCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aquatic Science & Fisheries Abstracts (ASFA), Geobase, INSPEC
  • Sayfa Sayıları: ss.527-536
  • Çukurova Üniversitesi Adresli: Evet

Özet

Micro-Deval abrasion, ultrasonic pulse velocity, uniaxial compression, point load index, Schmidt rebound hardness and Los Angeles abrasion tests were performed on 35 different rock types collected from different areas of Turkey, twelve of which were igneous, twelve of which were metamorphic and eleven of which were sedimentary. To investigate the possibility of predicting the micro-Deval (MDE) abrasion loss from mainly the ultrasonic pulse velocity (UPV), and then the uniaxial compressive strength (UCS), point load index (Is(50)) and Schmidt rebound hardness (RL), results of the tests were analyzed by simple and multiple regression analysis. A strong inverse correlation between MDE abrasion loss and UPV was found. In addition, meaningful inversely proportional relationships were determined between MDE and UCS, Is(50), and RL, as well.

Based on the simple regression model (coefficient of correlation r=0.845) derived from the results of this study, MDE was found to be suitable to predict practically and rapidly through UPV. New prediction models generated through simple and multiple regression analysis with substantial coefficients of correlation ranging from 0.845 to 0.887 using UCS, Is(50), and RL to predict MDE were also developed. Concluding comment is that derived equations can practically be used for the prediction of MDE abrasion loss from the UPV and UCS, Is(50), and RL.