4.7 Article

Efficient reliability analysis of earth dam slope stability using extreme gradient boosting method

Journal

ACTA GEOTECHNICA
Volume 15, Issue 11, Pages 3135-3150

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s11440-020-00962-4

Keywords

Ashigong earth dam; Extreme gradient boosting; Machine learning; Reliability analysis; Spatial variability

Funding

  1. National Key R&D Program of China [2019YFC1509600]
  2. Natural Science Foundation of Chongqing [cstc2019jcyj-bshX0043, cstc2019jcyj-bshX0032]
  3. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering [2019018]
  4. Science and Technology Research Program of Chongqing Municipal Education Commission [KJZD-K201900102]

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Reliability analysis approach provides a rational means to quantitatively evaluate the safety of geotechnical structures from a probabilistic perspective. However, it suffers from a known criticism of extensive computational requirements and poor efficiency, which hinders its application in the reliability analysis of earth dam slope stability. Until now, the effects of spatially variable soil properties on the earth dam slope reliability remain unclear. This calls for a novel method to perform reliability analysis of earth dam slope stability accounting for the spatial variability of soil properties. This paper develops an efficient extreme gradient boosting (XGBoost)-based reliability analysis approach for evaluating the earth dam slope failure probability. With the aid of the proposed approach, the failure probability of earth dam slope can be evaluated rationally and efficiently. The proposed approach is illustrated using a practical case adapted from Ashigong earth dam. Results show that the XGBoost-based reliability analysis approach is able to predict the earth dam slope failure probability with reasonable accuracy and efficiency. The coefficient of variations and scale of fluctuations of soil properties affect the earth dam slope failure probability significantly. Moreover, the earth dam slope failure probability is highly dependent on the selection of auto-correlation function (ACF), and the widely used single exponential ACF tends to provide an unconservative estimate in this study.

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