Development of machine learning models for forecasting the strength of resilient modulus of subgrade soil: genetic and artificial neural network approaches.

ABSTRACT: Accurately predicting the Modulus of Resilience (M_R) of subgrade soils, which exhibit non-linear stress-strain behaviors, is crucial for effective soil assessment. Traditional laboratory techniques for determining M_R are often costly and time-consuming. This study explores the efficacy of Genetic Programming (GEP), Multi-Expression Programming (MEP), and Artificial Neural Networks (ANN) in forecasting MR using 2813 data records while considering six key parameters. Several Statistical assessments were utilized to evaluate model accuracy. The results indicate that the GEP model consistently outperforms MEP and ANN models, demonstrating the lowest error metrics and highest correlation indices (R²). During training, the GEP model achieved an R² valu

SUBMITTER: Khawaja L 

PROVIDER: S-EPMC11303719 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature