Soil Compaction Parameters Prediction Based on Automated Machine Learning Approach

TL;DR

This paper introduces an AutoML approach to accurately predict soil compaction parameters like OMC and MDD across diverse soil types, improving efficiency over traditional labor-intensive methods.

Contribution

It applies AutoML to soil parameter prediction, demonstrating improved accuracy and generalization with heterogeneous datasets, and identifies XGBoost as the best performing algorithm.

Findings

XGBoost achieved 80.4% R-squared for MDD

XGBoost achieved 89.1% R-squared for OMC

AutoML enhances prediction accuracy and scalability

Abstract

Soil compaction is critical in construction engineering to ensure the stability of structures like road embankments and earth dams. Traditional methods for determining optimum moisture content (OMC) and maximum dry density (MDD) involve labor-intensive laboratory experiments, and empirical regression models have limited applicability and accuracy across diverse soil types. In recent years, artificial intelligence (AI) and machine learning (ML) techniques have emerged as alternatives for predicting these compaction parameters. However, ML models often struggle with prediction accuracy and generalizability, particularly with heterogeneous datasets representing various soil types. This study proposes an automated machine learning (AutoML) approach to predict OMC and MDD. AutoML automates algorithm selection and hyperparameter optimization, potentially improving accuracy and scalability. Through extensive experimentation, the study found that the Extreme Gradient Boosting (XGBoost) algorithm provided the best performance, achieving R-squared values of 80.4% for MDD and 89.1% for OMC on a separate dataset. These results demonstrate the effectiveness of AutoML in predicting compaction parameters across different soil types. The study also highlights the importance of heterogeneous datasets in improving the generalization and performance of ML models. Ultimately, this research contributes to more efficient and reliable construction practices by enhancing the prediction of soil compaction parameters.