Prediction of UCS values using basic geotechnical soil parameters via regression and Artificial Neural Networks ANN
Abstract
Unconfined Compressive Strength (UCS) test is a widely used lab procedure for assessing soil’s undrained shear strength. However, conventional lab testing is time-, cost-, and labor-intensive. This study evaluates predictive models for UCS using basic soil parameters. Soil mixtures were prepared and tested through several laboratory experiments, including Atterberg’s limits, particle size distribution, water content, bulk density using Harvard miniature compaction apparatus, and UCS. A total of 152 soil samples were utilized to train the prediction models. To achieve that, multi-linear regression (MLR), multi-nonlinear regression (MNLR), and backpropagation Artificial Neural Networks (ANN) were employed to relate the dependent variable UCS (predicted) to the independent geotechnical parameters (predictors). Results showed that the best model to predict the UCS values for soil using its soil parameters is the ANN-based model with R2 of 83% and ASE (Averaged Square Error) of 0.0029, followed by the nonlinear regression model with R2 = 49.2% and ASE of 3.63, and finally the MLR model with R2 = 44.5% and ASE of 3.92.
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