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ESTIMATION OF RESIDUAL FRICTION ANGLE OF CLAY SOILS USING ARTIFICIAL NEURAL NETWORKS MODELLING
Session: Physical and Numerical Modelling / Modélisation physique et numérique
Mostafa Abolfazl Zadeh , Clifton Associates Ltd. (Canada)
Amin Falamaki, Department of Civil Engineering – Payam Noor University, Shiraz (Iran)
Accurate estimation of site-specific soil strength parameters (e.g., the internal friction angle and cohesion) is challenging in geotechnical engineering due to the limitations and complexities associated with obtaining undisturbed soil samples and laboratory shear test analysis. The residual friction angle of clay soils is particularly important parameter in slope stability analysis, especially in case of pre-existing slip surfaces and large deformations, and is commonly approximated from Atterberg limits and grain size distribution using traditional regression analysis. In this study, we tested the reliability of Artificial Neural Networks (ANNs) in predicting the residual friction angle degrees of different soil types based on their Atterberg Limits, clay size fraction and normal stress. The main objective was to find a satisfactory relationship between input and actual measured values using artificial neural network models. The effect of the network geometry on the performance of the models was also assessed. Strong correlation factors (e.g., 0.99) for training and testing data sets in model MLP741 demonstrate that ANNs are powerful tools for predicting soil strength parameters.
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