Journal
UNDERGROUND SPACE
Volume 7, Issue 2, Pages 233-241Publisher
KEAI PUBLISHING LTD
DOI: 10.1016/j.undsp.2020.03.001
Keywords
Anisotropy; NGI-ADP; Basal heave; Braced excavation; Ensemble learning
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Funding
- Chongqing Construction Science and Technology Plan Project [2019-0045]
- Science and Technology Research Program of Chongqing Munici-pal Education Commission [KJZD-K201900102]
- Chongqing Engineering Research Center of Disaster Prevention and Control for Banks [SXAPGC18YB01, SXAPGC18ZD01]
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This paper investigates the effects of anisotropic soil parameters on the base stability of deep braced excavations using finite-element analysis and statistical regression analysis. The results show that anisotropy needs to be considered in the design of braced excavations, and XGBoost and RFR models can reasonably predict the factor of safety against basal heave.
A finite-element analysis considering the anisotropy for the undrained shear strength was performed to examine the effects of the total stress-based anisotropic model NGI-ADP (developed by Norwegian Geotechnical Institute based on the Active-Direct simple shear Passive concept) parameters on the base stability of deep braced excavations in clays. These parameters included the ratio of the plane strain passive shear strength to the plane strain active shear strength su/sAu, the ratio of the unloading/reloading shear modulus to the plane strain active shear strength Gur/sAu, the plane strain active shear strength sAu , the unit weight c, the excavation width B, the wall thickness b, and the wall penetration depth D. According to the numerical results for 1778 hypothetical cases, extreme gradient boosting (XGBoost) and random forest regression (RFR) were adopted to predict the factor of safety (FS) against basal heave for deep braced excavations. The results indicated that the anisotropic characteristics of soil parameters need to be considered when determining the FS against basal heave for braced excavation. XGBoost and RFR can yield a reasonable prediction of the FS. This paper presents a cutting edge application of ensemble learning methods in geotechnical engineering.
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