Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models

A comparison between deep learning and standalone models in predicting the compaction parameters of soil is presented in this research. One hundred and ninety and fifty-three soil samples were randomly picked up from two hundred and forty-three soil samples to create training and validation datasets, respectively. The performance and accuracy of the models were measured by root mean square error (RMSE), coefficient of determination (R2), Pearson product-moment correlation coefficient (r), mean absolute error (MAE), variance accounted for (VAF), mean absolute percentage error (MAPE), weighted mean absolute percentage error (WMAPE), a20-index, index of scatter (IOS), and index of agreement (IOA). Comparisons between standalone models demonstrate that the model MD 29 in Gaussian process regression (GPR) and model MD 101 in support vector machine (SVM) can achieve over 96% of accuracy in predicting the optimum moisture content (OMC) and maximum dry density (MDD) of soil, and outperformed other standalone models. The comparison between deep learning models shows that the models MD 46 and MD 146 in long short-term memory (LSTM) predict OMC and MDD with higher accuracy than ANN models. However, the LSTM models outperformed the GPR models in predicting the compaction parameters. The sensitivity analysis illustrates that fine content (FC), specific gravity (SG), and liquid limit (LL) highly influence the prediction of compaction parameters.