New Kriging methods for efficient system slope reliability analysis considering soil spatial variability

doi:10.1016/j.ress.2024.109989

2024-05-01

PRE

Shi-Ya Huang

Leilei Liu *

DOI：10.1016/j.ress.2024.109989

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摘要

En 中文

Recently, multiple Kriging (MK) metamodels have demonstrated their advantages in system slope reliability analysis. However, the inherent spatial variability of soil properties has not been considered due mainly to the curse of dimensionality. This study aims to extend the application of MK metamodels to efficient slope reliability analysis with spatially variable soils via developing three different but related representative novel MK methods. The most complex among them is the representative active learning Kriging (RALK) method, which first proceeds with the simulation of random fields of soil properties with a small number of independent variables using Karhunen-Loe`ve expansion method. Then, representative slip surfaces (RSSs) of a slope are identified to reduce the number of Kriging metamodels to be built, which is followed by using sliced inverse regression (SIR) to further reduce the number of independent variables. Thereafter, initial RALK metamodels are established for all RSSs with the dimension-reduced variables. Finally, a sequential sampling strategy is proposed to actively learn and update the RALK metamodels to further improve their efficiency. The other two methods are called MK-RSS and MK-RSS-SIR, which omit some steps compared with RALK method (MK-RSS-SIR omits active learning; MK-RSS omits active learning and SIR). The accuracy and efficiency of the three methods for slope reliability analysis are illustrated by considering two multi-layered soil slopes. The results show that RALK is suitable for slopes where the safety factor of one slip surface can be calculated separately, MK-RSS-SIR is suitable for slopes with simple conditions and more RSSs, and MK-RSS is suitable for slopes with complex conditions and fewer RSSs.

Keyword:

Slope reliability analysis

Spatial variability

Multiple Kriging

Representative slip surface

Sliced inverse regression

Karhunen-Loe `ve expansion