Predicting effective stress parameter of unsaturated soils using neural networks |
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| Institution: | 1. Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran;2. College of Engineering, Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman;3. Geomechanics Specialist, Rocscience lnc, Toronto, Canada;4. Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, Australia;1. College of Water Conservancy Engineering, Zhengzhou University, Zhengzhou, 450001, China;2. Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing, 210098, China;3. Henan Jiaoyuan Engineering Technology Group Co., Ltd, Zhengzhou, 450001, China;1. Department of Civil Engineering, Yeditepe University, ?stanbul, Turkey;2. 5A Mühendislik, ?stanbul, Turkey;3. MAG Mühendislik, ?stanbul, Turkey;4. Department of Civil Engineering, ?stanbul Technical University, ?stanbul, Turkey;1. Department of Civil and Environmental Engineering, Faculty of Engineering, Shiraz University, Shiraz, Iran;2. Department of Civil and Environmental Engineering, Faculty of Engineering, Western University, London, ON, Canada, N6A 5B9 |
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| Abstract: | In the effective stress equation for unsaturated soils proposed by Bishop, shear strength in these soils depends on the effective stress parameter, χ, a function of soil suction 1]. To estimate the shear strength of unsaturated soils, one must already know this parameter and its variation with soil suction. Though theories on the shear strength of unsaturated soils are consistent with experimental measurements, estimating the effective stress parameter directly from tedious laboratory tests is impractical. Thus, researchers have performed numerous intensive studies to effectively obtain the unsaturated shear strength using simplified empirical methods.This paper shows an adaptive learning neural network method for predicting this parameter, χ. The proposed network is a multilayer perceptron network with six neurons in the input layer representing the air entry value, the volumetric water content at residual and saturated conditions, the slope of soil water characteristic curve, the net confining stress and suction. The available literature uses a database prepared from triaxial shear test results to train and test the network. The results show the suitability of the proposed approach for estimating the effective stress parameter. Network analysis indicates that the χ-parameter depends strongly on the net mean stress. |
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