Predicting California Bearing Ratio with Ensemble and Neural Network Models: A Case Study from Turkiye

TL;DR

This paper develops and evaluates machine learning models, especially random forest, to predict the California Bearing Ratio from soil properties, demonstrating high accuracy and potential for practical geotechnical applications in Turkey.

Contribution

It introduces a comprehensive ML framework for CBR prediction using diverse soil data and compares multiple algorithms, highlighting the effectiveness of ensemble models like random forest.

Findings

Random forest achieved R2 of 0.83 on test data.

ML models outperform traditional laboratory testing in speed and cost.

The study promotes digital transformation in geotechnical engineering.

Abstract

The California Bearing Ratio (CBR) is a key geotechnical indicator used to assess the load-bearing capacity of subgrade soils, especially in transportation infrastructure and foundation design. Traditional CBR determination relies on laboratory penetration tests. Despite their accuracy, these tests are often time-consuming, costly, and can be impractical, particularly for large-scale or diverse soil profiles. Recent progress in artificial intelligence, especially machine learning (ML), has enabled data-driven approaches for modeling complex soil behavior with greater speed and precision. This study introduces a comprehensive ML framework for CBR prediction using a dataset of 382 soil samples collected from various geoclimatic regions in T\"urkiye. The dataset includes physicochemical soil properties relevant to bearing capacity, allowing multidimensional feature representation in a supervised learning context. Twelve ML algorithms were tested, including decision tree, random forest, extra trees, gradient boosting, xgboost, k-nearest neighbors, support vector regression, multi-layer perceptron, adaboost, bagging, voting, and stacking regressors. Each model was trained, validated, and evaluated to assess its generalization and robustness. Among them, the random forest regressor performed the best, achieving strong R2 scores of 0.95 (training), 0.76 (validation), and 0.83 (test). These outcomes highlight the model's powerful nonlinear mapping ability, making it a promising tool for predictive geotechnical tasks. The study supports the integration of intelligent, data-centric models in geotechnical engineering, offering an effective alternative to traditional methods and promoting digital transformation in infrastructure analysis and design.