On the interpretability of machine and deep learning techniques for predicting CBR of stabilized soil containing agro-industrial wastes.

ABSTRACT: Problematic soils pose challenges to light infrastructure, such as pavements, due to their swelling and collapse characteristics. Traditional soil stabilizers like cement and lime, while successful, have limits in cost, environmental impact, and energy use. This study explores agricultural and industrial byproducts as alternative stabilizers. It aims to determine the California Bearing Ratio (CBR) of stabilized soils using modern Machine and Deep Learning (MDL) techniques. MDL models used are Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), M5P Model Trees (M5P-MT), Extreme Gradient Boosting (XGBoost), Locally Weighted Polynomials (LWP), and Long Short-Term Memory (LSTM) networks. Two modeling approaches were created: approach I with 12 input variables and

SUBMITTER: Ghorbanzadeh S 

PROVIDER: S-EPMC12800152 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature