- Article
Conventional approaches for preliminary rock slope assessment lack precision, as they omit critical parameters related to slope geometry and failure mechanism, whereas comprehensive numerical analyses are computationally demanding. This study develops a surrogate modeling framework for rapidly predicting the performance of rock slopes governed by both rock mass and discontinuity-controlled failures. A synthetic database comprising 10,041 FEM/LEM models and 90,658 outputs was generated across five rock mass quality classes, three discontinuity classes, and systematically varied slope geometries representing a wide range of slope configurations. Mass failures were analyzed using shear strength reduction and limit equilibrium methods, while planar sliding, wedge failure, and block and flexural toppling were evaluated using limit equilibrium analyses. The resulting factor of safety, maximum total displacement, and maximum shear strain values were used to train feed-forward neural networks. The models achieved an average correlation coefficient of 0.96 for the factor of safety predictions of mass failures, while coefficients of 0.97 and 0.89 were obtained for maximum total displacement and maximum shear strain, respectively. Planar and toppling predictions also yielded an average correlation of 0.96. Independent benchmark cases produced an average coefficient of determination of 0.95, excluding wedge failure. Wedge predictions showed lower performance because valid wedge formation depends on restrictive combinations of discontinuity dip and dip direction. The proposed framework can efficiently screen extensive design spaces and identify critical scenarios based on detailed numerical investigation. The proposed method provides a computationally efficient preliminary assessment tool for slopes while explicitly accounting for multiple failure mechanisms.
13 November 2026





