An ANFIS–TLBO criterion for shear failure of rock joints

The strength behavior of a rock mass is often controlled by different discontinuities therein, along which the structural failures may threaten rock structures. Thus, the shear strength criteria for rock joints have always been a fundamental topic in rock mechanics. Knowing the different aspects of the rock joint shear strength based on the previously presented criteria, it is time to try different approaches other than the regression analysis in order to model the nonlinear behavior of rock joints. This research focuses on the estimation of the shear strength of rock joints using the computational intelligence. A total of 84 direct shear tests were first performed on replicas of natural rock fractures with various mechanical and morphological characteristics under several normal stress levels. Then, an adaptive neuro-fuzzy inference system (ANFIS) combined with a teaching–learning-based optimization (TLBO) algorithm was used to establish a new shear strength criterion. The results demonstrated that the ANFIS–TLBO criterion provided an accurate estimation of rock joint shear strength. A comparison between the ANFIS–TLBO criterion and the Barton’s empirical equation revealed a better performance of the suggested model in mapping the experimental data. The ANFIS–TLBO model’s residual indicated a random pattern, and its histogram exhibited a symmetric bell-shaped distribution around zero, supporting the appropriateness of the model.

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