Estimation of Some Geotechnical Indices of Soils using Machine Learning Techniques

Abstract

Adoption of a good estimation model for the prediction of sub soils properties before the commencement of a construction project, or at the preliminary stage of project planning is highly imperative. This will mitigate the most unexpected costs incurred during construction which are mostly geotechnical in nature. This research aims to use Machine Learning ML tools such as Multiple Linear Regression (MLR) Artificial Neural Network(ANN),Support Vector Machine(SVM), Random Forest(RF) andM5 Tree (M5P) in geotechnical Engineering with a view to correlate Optimum Moisture Content(OMC), Maximum Dry Density(MDD) and Soaked California Bearing Ratio(SCBR) and Unsoaked California Bearing Ratio (USCBR) from the measured index properties. The results from index properties classified the soils of the study area as  A-2-4, A-2-6, A-2-7 and A-7-5 for Ekiti Central Senatorial Districts (ECSD) and A-2-4, A-2-5, A-2-6, A-2-7, A-4, A-5, A-6 and A-7-5 for Ekiti South Senatorial Districts ( ESSD) while Ekiti Northern Senatorial Districts (ENSD) were classified as A-2-4, A-2-5, A-2-6, A-2-7, A-6 and A-7-6. Conversely. The strengths of the developed Machine Learning models have been examined in terms of regression coefficient (R²) and Root Mean Square Error (RMSE) values. It is found that all the five ML models predict OMC %, MDD, SCBR and USCBR close to the experimental value. However, the prediction of OMC %, MDD, SCBR and USCBR by RF is found better than other ML models deployed in this research.

DOI: 10.7176/CER/14-3-04

Publication date:May 31^st 2022