Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression
| Autor: | Wengang Zhang, Chongzhi Wu, Lin Wang, Anthony T. C. Goh, Runhong Zhang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Yield (engineering)
0211 other engineering and technologies 02 engineering and technology Building and Construction 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Stress (mechanics) Shear modulus Factor of safety Shear strength (soil) Geotechnical engineering Anisotropy Penetration depth Geology 021101 geological & geomatics engineering 0105 earth and related environmental sciences Civil and Structural Engineering Plane stress |
| Zdroj: | Underground Space. 7:233-241 |
| ISSN: | 2467-9674 |
| DOI: | 10.1016/j.undsp.2020.03.001 |
| Popis: | 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 s u P / s u A , the ratio of the unloading/reloading shear modulus to the plane strain active shear strength G ur / s u A , the plane strain active shear strength s u A , the unit weight γ, 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|