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Three-dimensional probabilistic stability analysis of an earth dam using an active learning metamodeling approach

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Abstract

The probabilistic stability analysis of earth dams is usually performed within two-dimensional (2D) computational models; thus, the 3D effect is ignored. Such a simplification could lead to biased estimates for the failure probability of dams especially for those located in narrow valleys. This article attempts to provide insights into the dam 3D probabilistic analysis by investigating the reliability of a real earth dam with field measurements and comparing the 3D results with the 2D ones. It is found that using a 3D computational model in a probabilistic analysis can give smaller estimates for the dam failure probability compared to the analyses based on a 2D section model. For the case study, the reduction of the failure probability is more significant in the case of a negative correlation between the soil shear strength parameters. The effects of using different deterministic mesh conditions on the dam reliability estimates are investigated as well. The results show that using a coarse mesh could lead to underestimated failure probabilities, especially for the 3D cases. The reliability analysis in this study is conducted by using an active learning surrogate modeling technique: adaptive sparse polynomial chaos expansions. This method is highly efficient in estimating failure probabilities and can provide an accurate approximation around the limit state surface by gradually adding well-selected samples into the current training set. The global sensitivity indices (Sobol) are also available in this method, so the contribution of each soil property to the variation of the dam safety factor is quantified and presented.

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Availability of data and material

The data used in this study were provided by a construction company. Restrictions apply to the availability of these data, which were used under license for this study. Data could be available on request with the permission of this company.

Code availability

The codes developed in this study are available on request.

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Acknowledgements

The authors would like to acknowledge the supports from the National Natural Science Foundation of China (51978322) and the China Scholarship Council (201608070075).

Funding

The first author is financially supported by the China Scholarship Council under the grant number of 201608070075.

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Authors and Affiliations

  1. CNRS, Grenoble INP, Institute of Engineering Univ, 3SR, Univ. Grenoble Alpes, F-38000, Grenoble, France

    Xiangfeng Guo & Daniel Dias

  2. Antea Group, Antony, France

    Daniel Dias

  3. RECOVER, INRAE, Aix-Marseille University, F-13182, Aix-en-Provence, France

    Claudio Carvajal & Laurent Peyras

  4. Institut Pascal, CNRS, Univ. Clermont Auvergne, F-63000, Clermont-Ferrand, France

    Pierre Breul

  5. 3SR laboratory, 1270, Rue da la Piscine, F-38610, Gières, France

    Daniel Dias

Authors
  1. Xiangfeng Guo
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  2. Daniel Dias
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  3. Claudio Carvajal
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  4. Laurent Peyras
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  5. Pierre Breul
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Contributions

Conceptualization: Claudio Carvajal and Laurent Peyras; Methodology: Xiangfeng Guo and Daniel Dias; Formal analysis and investigation: Xiangfeng Guo and Pierre Breul; Writing — original draft preparation: Xiangfeng Guo; Writing — review and editing: Daniel Dias, Claudio Carvajal, Laurent Peyras, and Pierre Breul; Supervision: Daniel Dias, Claudio Carvajal, Laurent Peyras, and Pierre Breul.

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Correspondence to Daniel Dias.

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Highlights

• 3D reliability analysis of a real earth dam conducted using field measurements.

• An active learning metamodeling technique is employed.

• 3D geometry dam reliability effect is discussed considering different factors.

• A global sensitivity analysis is performed.

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Guo, X., Dias, D., Carvajal, C. et al. Three-dimensional probabilistic stability analysis of an earth dam using an active learning metamodeling approach. Bull Eng Geol Environ 81, 40 (2022). https://doi.org/10.1007/s10064-021-02512-y

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  • DOI: https://doi.org/10.1007/s10064-021-02512-y

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