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Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes
Response surface methods (RSMs) are attractive approaches for slope reliability analysis because such methods can integrate deterministic numerical slope stability evaluation and reliability analysis. For a slope in layered soils, its performance function is generally nonlinear and the system failure probability could be larger than the failure probability of the most critical slip surface. In this study, the applicability of the kriging-based RSM for system reliability of soil slopes is assessed through its application to two slopes with obvious system effects. It is found that the kriging method combined with Monte Carlo simulation (MCS) can deliver accurate system failure probability estimation. For comparison, the classical RSM based on the iterative local approximation of the performance function may fail to detect the most critical slip surface. The classical RSM can only calculate the failure probability of one slip surface even if the first-order reliability method or MCS is used. Even when the same calibration samples are used, the second-order polynomial-based RSM is not as accurate as the kriging-based RSM. The results in this study show that the kriging-based RSM is an advantageous and promising approach for calculating the system reliability of soil slopes.
Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes
Response surface methods (RSMs) are attractive approaches for slope reliability analysis because such methods can integrate deterministic numerical slope stability evaluation and reliability analysis. For a slope in layered soils, its performance function is generally nonlinear and the system failure probability could be larger than the failure probability of the most critical slip surface. In this study, the applicability of the kriging-based RSM for system reliability of soil slopes is assessed through its application to two slopes with obvious system effects. It is found that the kriging method combined with Monte Carlo simulation (MCS) can deliver accurate system failure probability estimation. For comparison, the classical RSM based on the iterative local approximation of the performance function may fail to detect the most critical slip surface. The classical RSM can only calculate the failure probability of one slip surface even if the first-order reliability method or MCS is used. Even when the same calibration samples are used, the second-order polynomial-based RSM is not as accurate as the kriging-based RSM. The results in this study show that the kriging-based RSM is an advantageous and promising approach for calculating the system reliability of soil slopes.
Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes
Zhang, J. (Autor:in) / Huang, H. W. (Autor:in) / Phoon, K. K. (Autor:in)
Journal of Geotechnical and Geoenvironmental Engineering ; 139 ; 651-655
01.08.2012
52013-01-01 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes
| Online Contents | 2013
Application of the Kriging-Based Response Surface Method to the System Reliability of Soil Slopes
| British Library Online Contents | 2013
| British Library Online Contents | 2017