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This technical article helps identify the optimum performance AI model for predicting compaction parameters of soil. A comparative study is mapped between regression analysis (RA), Gaussian process regression (GPR), decision tree (DT), support vector machine (SVM), and artificial neural networks (ANNs) approaches using 59 soil datasets. The soil dataset consists of soil properties such as gravel content, silt content, sand content, specific gravity, clay content, plasticity index, and liquid limit. The soil properties are used as input parameters to develop the AI model to predict soil optimum moisture content and maximum dry density. The RA, GPR, SVM, DT, and ANN models are designated as MLR_X, GPR_X, SVM_X, DT_X, ANN_X, where the X is OMC and MDD. The performance of MLR_OMC, GPR_OMC, SVM_OMC, DT_OMC, LMNN_OMC, and GDANN_OMC is 0.9714, 0.9867, 0.9689, 0.9832, 0.9435, and 0.9520, respectively. Similarly, the performance of MLR_MDD, GPR_MDD, SVM_MDD, DT_MDD, LMNN_MDD, and GDANN_MDD is 0.9512, 0.9854, 0.9482, 0.9199, 0.8679, and 0.9395, respectively. Based on the performance of AI models, the GPR_OMC and GPR_MDD models are identified as the optimum performance model to predict the soil maximum dry density (MDD) and optimum moisture content (OMC). The predicted OMC and MDD are compared with laboratory OMC and MDD, and it is found that the GPR_OMC and GPR_MDD model has the potential to predict soil compaction parameters.


Regression Analysis, Gaussian Process Regression, Support Vector Machine, Artificial Neural Network, Compaction Parameters of Soil
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