Application of Machine Learning Techniques to Estimate Unsoaked California Bearing Ratio in Ekiti Central Senatorial District

This paper investigates the relationship between soil physical properties and the Un-soaked California Bearing Ratio (USCBR) of soil found in Ekiti State Central Senatorial District (ESCSD), which includes Natural Moisture Content (NMC%) Percentage Fines, Specific Gravity (SG) and Consistency Limits (LL%, PL%, & PI %). The database was prepared in the laboratory by conducting tests on ninety-nine (99) soil samples which were obtained in a burrowed pit found in the Central Senatorial District of Ekiti State. An R version 4.0.5 and R studio version 1.2.5033 was used to analyze the Artificial Neural Networks (ANNs) and Least Square Regression (LSR) in order to develop a simplified CBR model. In both models, independent layer containing six nodes (soil physical properties) and the dependent layer containing a single node (i.e. CBR) were taken. The descriptive analysis for training and testing was performed; boxplots of the variables were plotted and; sensitivity analysis was carried out. The capacity of the developed equation was evaluated in terms of error metrics MSE and RMSE. The analysis showed that both ANN and MLR models predicted CBR close to the laboratory value. However, the model without the percentage passing sieve 200 (MIC) is the best, having Akaike Information Criterion and Bayesian Information Criterion values of 614.1707 and 627.5754 respectively, from the error metrics analysis, the results showed that PL and LL are the most influential variable that affects the developed CBR model's output. From the foregoing its concluded that the study has shown a relationship between the CBR value of Ekiti Central Senatorial District soil and its basic soils properties using machine learning techniques, also the developed CBR model will be useful tool to Civil engineers, geotechnical engineers and construction industry within the study area particularly in their preliminary stage of their project.