Correlations of SMR-Qslope Data in Stability Classification of Discontinuous Rock Slope: A Modified Relationship Considering the Iranian Data

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 Naïve-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 / R² = 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 correlação entre os índices 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.