Abstract

The present research introduces the best architectural relevance vector machine (RVM) model for predicting the compaction parameters of soil. The two types of RVM models, i.e., single kernel function-based (SRVM) and dual kernels (parallel) function-based (DRVM), have been constructed in this study. However, the RVM is a kernel function-based approach. Therefore, linear, gaussian, laplacian, and polynomial kernel functions have been implemented in these models. Each model has been optimized by each Genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. For this purpose, 59 soil samples have been collected from the literature. The root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) statistical tools have been used to measure the performance and accuracy of models. From the overall analysis, models MC10 and MD12 have predicted OMC (RMSE = 0.8194%, R = 0.9956, MAE = 0.7920%) and MDD (RMSE = 0.1310g/cc, R = 0.9941, MAE =0.0008g/cc) better than other RVM models. It has also been observed that the DRVM model predicts the compaction parameters better than the SRVM models. The GA algorithm is robust in predicting OMC prediction, and the PSO algorithm is robust in MDD prediction. The score analysis also confirms the robustness of the dual kernel function based DRVM models for predicting OMC and MDD of soil. The sensitivity analysis demonstrates that compaction parameter prediction is strongly influenced by the specific gravity, liquid limit, and plasticity index of soil.