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Time-dependent reliability analysis of unsaturated slopes under rapid drawdown with intelligent surrogate models
https://orcid.org/http://orcid.org/0000-0002-6281-9076
Slope stability in reservoirs depends on time-dependent triggering factors such as fluctuations of the groundwater level and precipitation. This paper assesses the stability of reservoir slopes over time, accounting for the uncertainty of the shear strength and hydraulic parameters. An intelligent surrogate model has been developed to reduce the computational effort. The capability of two machine learning algorithms, namely Support Vector Regression and Extreme Gradient Boosting, is considered to obtain the relationship between geomechanical parameters and the factor of safety. The probability of failure of a hypothetical reservoir slope is estimated employing Monte Carlo simulations for different scenarios of drawdown velocity. A sensitivity analysis is conducted to investigate the influence of the geomechanical parameters, regarded as random variables, on the probability of failure. The results revealed that the coefficient of variation in the effective friction angle and the correlation between effective cohesion and friction angle have the highest impact on the probability of failure. The intelligent surrogate model can predict the factor of safety of reservoir slopes under rapid drawdown with high accuracy and enhanced computational efficiency.
Time-dependent reliability analysis of unsaturated slopes under rapid drawdown with intelligent surrogate models
https://orcid.org/http://orcid.org/0000-0002-6281-9076
Slope stability in reservoirs depends on time-dependent triggering factors such as fluctuations of the groundwater level and precipitation. This paper assesses the stability of reservoir slopes over time, accounting for the uncertainty of the shear strength and hydraulic parameters. An intelligent surrogate model has been developed to reduce the computational effort. The capability of two machine learning algorithms, namely Support Vector Regression and Extreme Gradient Boosting, is considered to obtain the relationship between geomechanical parameters and the factor of safety. The probability of failure of a hypothetical reservoir slope is estimated employing Monte Carlo simulations for different scenarios of drawdown velocity. A sensitivity analysis is conducted to investigate the influence of the geomechanical parameters, regarded as random variables, on the probability of failure. The results revealed that the coefficient of variation in the effective friction angle and the correlation between effective cohesion and friction angle have the highest impact on the probability of failure. The intelligent surrogate model can predict the factor of safety of reservoir slopes under rapid drawdown with high accuracy and enhanced computational efficiency.
Time-dependent reliability analysis of unsaturated slopes under rapid drawdown with intelligent surrogate models
https://orcid.org/http://orcid.org/0000-0002-6281-9076
Slope stability in reservoirs depends on time-dependent triggering factors such as fluctuations of the groundwater level and precipitation. This paper assesses the stability of reservoir slopes over time, accounting for the uncertainty of the shear strength and hydraulic parameters. An intelligent surrogate model has been developed to reduce the computational effort. The capability of two machine learning algorithms, namely Support Vector Regression and Extreme Gradient Boosting, is considered to obtain the relationship between geomechanical parameters and the factor of safety. The probability of failure of a hypothetical reservoir slope is estimated employing Monte Carlo simulations for different scenarios of drawdown velocity. A sensitivity analysis is conducted to investigate the influence of the geomechanical parameters, regarded as random variables, on the probability of failure. The results revealed that the coefficient of variation in the effective friction angle and the correlation between effective cohesion and friction angle have the highest impact on the probability of failure. The intelligent surrogate model can predict the factor of safety of reservoir slopes under rapid drawdown with high accuracy and enhanced computational efficiency.
Time-dependent reliability analysis of unsaturated slopes under rapid drawdown with intelligent surrogate models
Acta Geotech.
Guardiani, Carlotta (Autor:in) / Soranzo, Enrico (Autor:in) / Wu, Wei (Autor:in)
Acta Geotechnica ; 17 ; 1071-1096
01.04.2022
26 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Machine learning , Rapid drawdown , Reliability , Slope stability , Surrogate model , Unsaturated Engineering , Geoengineering, Foundations, Hydraulics , Solid Mechanics , Geotechnical Engineering & Applied Earth Sciences , Soil Science & Conservation , Soft and Granular Matter, Complex Fluids and Microfluidics
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