Prediction of Geotechnical Properties of Soil using Artificial Intelligence Framework 
Jitendra Khatti1, Kamaldeep Singh Grover2
1Jitendra Khatti*, PhD Fellow, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan) India.
2Dr. Kamaldeep Singh Grover, Professor, Department of Civil Engineering, Rajasthan Technical University, Kota (Rajasthan) India.
Manuscript received on November 16, 2021. | Revised Manuscript received on November 22, 2021. | Manuscript published on November 30, 2021. | PP: 218-227 | Volume-10 Issue-4, November 2021. | Retrieval Number: 100.1/ijrte.D66251110421 | DOI: 10.35940/ijrte.D6625.1110421
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Consistency limits, Deep Learning, Geotechnical Properties, Hybrid Learning, Machine Learning,