A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Slope Stability Analysis Using Bayesian Markov Chain Monte Carlo Method
Abstract Slope stability analysis is an enduring research topic in the engineering and academic sectors. Accurate prediction of the factor of safety (FOS) of slopes, their stability and their performance is not an easy task. The current study aims at predicting the FOS on the geometrical and geotechnical input parameters [unit weight (γ), cohesion (C), slope angle (β), height (H), angle of internal friction (φ) and pore pressure ratio (ru)]. The Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of models using the WinBUGS package, freely available software. The WinBUGS software is implemented to identify the most appropriate models for estimating the FOS among twenty (20) candidate models that have been proposed. The models were applied to available data given in open source literature. The unknown parameters of the models are considered as random variables. The WinBUGS software which uses Bayesian analysis of complex statistical models and MCMC techniques is employed to compute the posterior predictive distributions. The mean values of the model parameters obtained via MCMC simulations are considered for the model prediction performance evaluation. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination ($ R^{2} $) and mean square error. Overall, the results indicate that the proposed FOS model possesses satisfactory predictive performance.
Slope Stability Analysis Using Bayesian Markov Chain Monte Carlo Method
Abstract Slope stability analysis is an enduring research topic in the engineering and academic sectors. Accurate prediction of the factor of safety (FOS) of slopes, their stability and their performance is not an easy task. The current study aims at predicting the FOS on the geometrical and geotechnical input parameters [unit weight (γ), cohesion (C), slope angle (β), height (H), angle of internal friction (φ) and pore pressure ratio (ru)]. The Markov chain Monte Carlo (MCMC) methods have become a ubiquitous tool in Bayesian analysis. This paper implements MCMC methods for Bayesian analysis of models using the WinBUGS package, freely available software. The WinBUGS software is implemented to identify the most appropriate models for estimating the FOS among twenty (20) candidate models that have been proposed. The models were applied to available data given in open source literature. The unknown parameters of the models are considered as random variables. The WinBUGS software which uses Bayesian analysis of complex statistical models and MCMC techniques is employed to compute the posterior predictive distributions. The mean values of the model parameters obtained via MCMC simulations are considered for the model prediction performance evaluation. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination ($ R^{2} $) and mean square error. Overall, the results indicate that the proposed FOS model possesses satisfactory predictive performance.
Slope Stability Analysis Using Bayesian Markov Chain Monte Carlo Method
Fattahi, Hadi (author) / Zandy Ilghani, Nastaran (author)
2020
Article (Journal)
Electronic Resource
English
BKL:
57.00$jBergbau: Allgemeines
/
38.58
Geomechanik
/
57.00
Bergbau: Allgemeines
/
56.20
Ingenieurgeologie, Bodenmechanik
/
38.58$jGeomechanik
/
56.20$jIngenieurgeologie$jBodenmechanik
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