Correlations of SMR-Q(slope) Data in Stability Classification of Discontinuous Rock Slope: A Modified Relationship Considering the Iranian Data

Azarafza M., KOÇKAR M. K., Zhu H.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING, cilt.40, sa.4, ss.1751-1764, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 4
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s10706-021-01991-w
  • Dergi Adı: GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Computer & Applied Sciences, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1751-1764
  • Anahtar Kelimeler: Rock slope engineering, Slope mass rating, Q(slope), Empirical relationship, Artificial intelligence, MASS RATING SMR, PARAMETERS
  • Hacettepe Üniversitesi Adresli: Evet

Özet

The preliminary responses on stability assessment are very effective in discontinuous rock slope stabilisations, which geo-mechanical empirical classifications/approaches can be evaluated. Although there are several geomechanical classifications for rock mass characteristics, they still have some complexities and shortcomings. In this paper, correlations based on two flexible classification systems, namely slope mass rating (SMR) and Barton's Q(slope), has been discussed as a connection between failure (i.e., safety factor, reliability) and stability conditions (i.e., failure mechanism, support system) which were performed in 300 road/railway slope cases from 12 provinces in Iran. The empirical SMR-Q(slope) relationship has been used for sedimentary rocks defined lithologically as limestone, marlstone, sandstone, and claystone. The method was used to provide the empirical link to the primary design for discontinuous rock slopes. To this end, after field investigations and the necessary geo-engineering data gathered on the studied slopes. The information used to provide the SMR and Q(slope) retirements prepare SMR-Q(slope) relation for Iranian data. The artificial intelligence techniques including k-nearest neighbours, support vector machine, Gaussian process, decision tree, random forest, multilayer perception, and Naive-Bayes classifiers have been used for detailed classifications. The accurate correlations provided have been implemented and revised in the Python programming language. According to the learning model results, SMR-Q(slope) equation for Iranian data has been developed as SMR = 6.699 ln (Q(slope)) + 58.99 / R-2 = 0.62 with 0.95 (95%) accuracy. The evaluated results were verified and controlled by Jorda-Bordehore et al. (Stability assessment of rock slopes using empirical approaches: comparison between slope mass rating and Q-slope, 2018) and Maion (Proposta de correlacao entre os indices SMR e Q-slope , 2019) empirical relationships as a basic table. By comparing these studies' results, it can be stated that the present study has improved the accuracy of SMR-Q(slope) relationship.