Volume 10 Issue 2
Jan.  2021
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Leilei Liu, Shaohe Zhang, Yung-Ming Cheng, Li Liang. Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines[J]. Geoscience Frontiers, 2019, 10(2): 671-682. doi: 10.1016/j.gsf.2018.03.013
Citation: Leilei Liu, Shaohe Zhang, Yung-Ming Cheng, Li Liang. Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines[J]. Geoscience Frontiers, 2019, 10(2): 671-682. doi: 10.1016/j.gsf.2018.03.013

Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines

doi: 10.1016/j.gsf.2018.03.013
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The work presented in this paper was supported by The Hong Kong Polytechnic University through the project RU3Y and the Research Grant Council through the project PolyU 5128/13E, National Natural Science Foundation of China (Grant No. 51778313) and Cooperative Innovation Center of Engineering Construction and Safety in Shangdong Blue Economic Zone. These financial supports are gratefully acknowledged.

  • Received Date: 2017-09-20
  • Rev Recd Date: 2018-01-29
  • Publish Date: 2021-01-07
  • This study aims to extend the multivariate adaptive regression splines (MARS)-Monte Carlo simulation (MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure (Pf ) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loève expansion. MCS is subsequently performed on the established MARS model to evaluate Pf. Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach. Results showed that the proposed approach can estimate the Pf of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of Pf. Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the Pf. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs.
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