Bayesian back analysis of landslides considering slip surface uncertainty
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Yankun Wang I Jinsong Huang I Huiming Tang I Cheng Zeng
Bayesian back analysis of landslides considering slip surface uncertainty
Abstract Previous studies about probabilistic back analysis for shear strength parameters of landslides generally adopted a fixed slip surface. This setting may lead to unreliable results due to the uncertainty of slip surface location speculated by limited observations. Based on Bayes’ theorem, this paper proposes a probabilistic framework for the back analysis of landslides considering slip surface uncertainty. The posterior distributions of shear strength parameters in Bayesian inference are solved by Markov chain Monte Carlo simulation method. To improve computational efficiency, a response surface function based on extreme learning machine is constructed to approximate the relationship between shear strength parameters and the corresponding factor of safety and critical slip surface. A synthetic slope, for which the actual shear strength parameters and slip surface are known, is used to compare the proposed and traditional methods. The effects of measurement error of slip surface and prior distribution of shear strength parameters on probabilistic back analysis results are also investigated. Results show that the shear strength parameters obtained from traditional probabilistic back analyses neglecting slip surface uncertainty significantly deviate from actual values, and are greatly affected by prior mean of shear strength parameters. The proposed method performs better than traditional method and is less affected by the prior distributions of shear strength parameters, and the smaller the measurement error of slip surface, the higher the Bayesian back analysis accuracy. A practical landslide is applied to further verify the effectiveness of the proposed method. Keywords Bayesian back analysis . Shear strength . Slip surface uncertainty . Landslide . Extreme learning machine Introduction Back analysis for shear strength parameters of a failed slope is a practical and widely used strategy in remedial design or stability evaluation of the analogous slopes (Duncan et al. 2014). The failed slope can be regarded as a large-scale in situ experiment. When the slope failed, the factor of safety (FS) equals unity at the time of failure, and portions of actual slip surface can be obtained. The back calculated parameters incorporate all the in situ information are therefore more representative than the parameters obtained from laboratory test. Generally, back analysis methods can be divided into two categories, i.e., deterministic methods and probabilistic methods. Deterministic methods try to find a set of certain values satisfying the observed information. For example, Wesley and Leelaratnam (2001) and Jiang and Yamagami (2006, 2008) and Gao (2016) implemented deterministic back analysis of the shear strength parameters by fully utilizing the information of FS and location of observed slip surface. Deterministic back analysis methods are simple and easy to implement. However, these methods
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