Abstract
The present research work is carried out to predict the geotechnical properties (consistency limits, OMC, and MDD) of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM). The models of machine learning (SVM, GPR), hybrid learning (RVM), and deep learning (ANNs) are constructed in MATLAB R2020a with different configurations. The models of RA are built using the Data Analysis Tool of Microsoft Excel 2019. The input parameters of AI models are gravel, sand, silt, and clay content. The correlation coefficient is calculated for pair of soil datasets. The correlation shows that sand, silt, and clay content play a vital role in predicting soil's liquid limit and plasticity index. The performance of constructed AI models is compared to determine the optimum performance models. The limited datasets of soil are used in this study. Therefore, artificial neural networks and relevance vector machines could not perform well. Based on the performance of AI models, the Gaussian process regression outperformed the RA, SVM, ANNs, and RVM AI technologies. Hence, the GPR AI approach can predict the geotechnical properties of soil by gravel, sand, silt, and clay content. The Monte-Carlo global sensitivity analysis is also performed, and it is observed that the prediction of geotechnical properties of soil is affected by sand and clay content
Highlights
The present research work is carried out to predict the geotechnical properties of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM)
Support vector machine, Gaussian regression process, artificial neural network, and relevance vector machine models have been constructed in this research work to predict the consistency limits and compaction parameters of soil
The liquid limit, plasticity index, optimum moisture content (OMC), and maximum dry density (MDD) of soil are more influenced by sand and clay content
Summary
The present research work is carried out to predict the geotechnical properties (consistency limits, OMC, and MDD) of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM). The correlation shows that sand, silt, and clay content play a vital role in predicting soil's liquid limit and plasticity index. Most of the studies were carried out to predict soil CBR, compaction, and strength parameters using consistency limits, particle content, and other properties of soil. The published work shows that the consistency limits and compactive parameters of soil are affected by gravel, sand, silt, and clay content. The literature shows that artificial intelligence approaches have not been frequently applied to predict consistency limits, OMC, and MDD using gravel, sand, silt, and clay content. Perform the Monte-Carlo global sensitivity analysis to study the effect of input variables on output variables
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