Stochastic kinematic analysis of rock slope failure angle based on multi algorithm optimization, a case study of the proposed bridge project

Due to the randomness and complexity of the discontinuity distribution, it is always complicated to analyse the three-dimensional stability problem of discontinuity-controlled rock slope in the field of engineering geology. Specifically, when restricted by poor sampling conditions, the discontinuity data obtained are very limited. To solve this problem, we propose a stochastic kinematic method to estimate the slope failure angle based on limited data. Taking the high-steep rock slope on the right bank of a proposed bridge as an example, the stochastic kinematic method based on multiple algorithms (Monte Carlo simulation, Latin hypercube, and Bayesian estimation) is used to analyse the failure angles of potentially unstable blocks in two modes of plane and wedge failure. After statistical optimisation, the slope failure angles are determined to be 65.69 degrees (plane failure) and 58.65 degrees (wedge failure); this approach is more objective than the empirical formula method and can be used as the basis for judging the slope stability. The method proposed in this study fully considers the randomness of discontinuity distribution and combination, and can use limited sample data to realise failure angle analysis and stability judgement. The result is important as a reference of significance for engineering construction and disaster prevention.

Automated summary

Due to the limited availability of discontinuity data in engineering geology, a stochastic kinematic method based on Monte Carlo simulation, Latin hypercube, and Bayesian estimation was proposed to estimate the slope failure angle in a discontinuity-controlled rock slope. After statistical optimization, the slope failure angles were determined to be 65.69 degrees (plane failure) and 58.65 degrees (wedge failure). This method provides a more objective approach for slope stability analysis and can be used as a basis for engineering construction and disaster prevention.