Spatial variation of shear strength properties incorporating auxiliary variables

Sabrina Chui Yee Ip, Alfrendo Satyanaga, harianto rahardjo

Research output: Contribution to journalArticlepeer-review

Abstract

Soil shear strength is a critical parameter in slope stability. Shear strength properties may vary significantly over
large areas. Thus, the spatial estimates of shear strength properties are necessary for deterministic slope susceptibility
mapping over large areas. However, measurements of shear strength parameters are often limited as
compared to other soil properties such as Atterberg limit, bulk density and grain size distribution. Multivariate
methods have been shown to improve prediction accuracy, but these methods have rarely been used to predict
shear strength. In this study, attempts were made to evaluate the effectiveness of using the aforementioned soil
properties in predicting the spatial variation of shear strength properties: effective cohesion (c’) and effective
friction angle (ϕ’). The performance of ordinary kriging (OK), Random Forest (RF) and regression kriging (RK)
in predicting c’ and ϕ’ of residual soils in Singapore were compared and evaluated. In addition, the sensitivity
of the three methods to the sample size was investigated. The results of RF analysis revealed that the northing
coordinate and percentage of fines were the most important variables for predicting ϕ’. The spatial coordinates
and ϕ’ were also important variables for predicting c’. The predicted c’ and ϕ’ using RF and RK resulted in higher
spatial heterogeneity than OK. Overall, RF had the smallest error as compared to OK and RK in predicting c’ and
ϕ’ at all sample sizes, except for the prediction of ϕ’ using the largest sample size. This study also showed that
RF and RK were more sensitive to sample size than OK. These results highlight the benefits of using auxiliary
variables when mapping shear strength properties.
Original languageEnglish
JournalCatena
Publication statusAccepted/In press - Jan 2021

Keywords

  • Regression kriging
  • tial variability
  • Sampling density
  • Random forest
  • Soil shear strength

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