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It normally takes four days or more in the laboratory to determine the soaked California bearing ratio (CBR) of lateritic soil samples. Furthermore, it is virtually impossible to conduct a large number of tests for a significant project within a short
time frame. Alternative approaches, like forecasting models for soaked California bearing ratio, may therefore be used. The soaked California bearing ratio of A-2 lateritic soil was estimated using the Support Vector Machine (SVM) and Random
Forest in western Niger Delta. A total of 52 dataset samples, comprising the plastic limit, liquid limit, plasticity index, percent of sand that passed through a 200-mesh sieve, moisture content, maximum dry density, and soaked CBR from a published
source and laboratory results from a field investigation, were collected. Wakaito Environment for Knowledge (WEKA) 3.9.5 software was used to investigate the potential of SVMs and random forests to predict the soaked CBR of A-2 lateritic soil. It was found that support vector machine models outperformed random forest models in terms of estimating the soaked California bearing ratio of A-2 lateritic soil. The points on a scatter plot showing outputs from the training, cross-validation and percentage split 65% processes are very close to equality line.