Revisiting correlations between index properties and residual friction angle of natural soils using artificial neural networks

Abstract

In order to minimize time and capital investment for preliminary designs of active landslides, researchers have proposed correlation charts relating soil index properties such as LL, PI and CF to residual friction angle. However, estimated residual friction angle shows large variations by chart type and soil properties. This is because the databases that have been used to establish these correlation charts belongs to different soil types and obtained using different testing apparatus with different shearing rates. Therefore, they cannot be generalized. In this study, an artificial neural network (ANN) model was developed to evaluate databases for soils from Britain, China, Japan and the Pacific Islands to determine which soil index properties give best estimate of residual friction angle of these soils. The ANN model developed was further validated using Skempton's (1985) data. The results indicate that there exist a reasonable correlation between soil index properties such as LL, PI, and CF and residual friction, providing that the soils are tested in similar manner and have similar mineralogy. The sensitivity analysis results indicated that residual friction angle is most dependent on CF of soils. The results also show that the ANN model developed is a powerful for predicting residual friction angle of soils using soil index properties.

Keywords:

• artificial neural networks
• clay fraction
• liquid limit
• plastic index
• residual friction angle
• soil indexes