Estimation of effective cohesion using artificial neural networks based on index soil properties: A Singapore case
Kim Y. Satyanaga A. Rahardjo H. Park H. Sham A.W.L.
August 2021Elsevier B.V.
Engineering Geology
2021#289
This study presents a development of a multi-layer perceptron (MLP) model to spatially estimate and analyze the variability of effective cohesion for residual soils that are commonly associated with rainfall-induced slope failures in Singapore. A number of soil data were collected from the various construction sites, and a set of qualified Nanyang Technological University (NTU) data were utilized to determine a criterion for data selection. Four index properties (i.e., percentage of fines and coarse fractions, liquid and plastic limits) were used as training parameters to estimate the effective cohesion of residual soils from different geological formations. Ordinary kriging analyses were carried out to analyze the spatial distribution and variability of effective cohesion. As a result, the appropriate effective cohesions can be estimated using the MLP model with the incorporation of the selected values of measured effective cohesion as training data and four index soil properties as input data. In the combination of estimated and measured effective cohesions, the spatial analysis using Kriging method can clearly differentiate the variations in effective cohesion with respect to the different geological formations.
Artificial neural networks , Effective cohesion , Index properties , Residual soil
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School of Engineering, University of Glasgow Singapore, Singapore
Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Kabanbay Batyr Ave., 53, 010000, Nur-Sultan, Kazakhstan
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
Institute of Infocomm Research, Agency for Science, Technology and Research, Singapore
Special Functions Group, Enforcement & Structural Inspection Department, Building and Construction Authority, Singapore
School of Engineering
Department of Civil and Environmental Engineering
School of Civil and Environmental Engineering
Institute of Infocomm Research
Special Functions Group
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