4.6 Article

Design charts for reliability assessment of rock bedding slopes stability against bi-planar sliding: SRLEM and BPNN approaches

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/17499518.2020.1815215

Keywords

Rock bedding slope; bi-planar sliding; reliability assessment; back-propagation neural network; First-Order reliability method; safety factor

Funding

  1. National Key R&D Program of China [2019YFC1509600]
  2. Chongqing City Construction Investment (Group) Co., Ltd. [2019-04]
  3. High-end Foreign Expert Introduction program [G20190022002]

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This study proposes a framework for probabilistic assessment on rock bedding slope stability against bi-planar sliding. A surrogate model for factor of safety against bi-planar sliding is developed based on back-propagation neural network (BPNN), which is implemented into the Excel Spreadsheet First-Order Reliability Method for reliability assessment.
Bi-planar sliding is one of main instabilities for bedding rock slope and is mainly dominated by the geometrical and strength properties of the weak structural plane. Traditional stability evaluation combines limit equilibrium method (LEM) with shear strength reduction (SSR) to derive the deterministic safety factor against slope stability. However, considering the uncertainties inherent in the geometrical and strength properties of the weak structural plane, as well as the variations of rock mass properties, the safety factor of rock bedding slopes against bi-planar sliding cannot be deterministically calculated. This study proposes a framework for probabilistic assessment on rock bedding slope stability against bi-planar sliding. Surrogate model for factor of safety against bi-planar sliding from LEM and SSR is developed based on back-propagation neural network (BPNN). The BPNN model, together with the design variables is implemented into the Excel Spreadsheet First-Order Reliability Method for the reliability assessment.

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