Abstract:
This article investigates the importance of moisture content in cement-stabilized
earth blocks (CSEBs) and explores methods for their prediction using machine learning.
A key aspect of the research is the development of accurate moisture content
prediction models. The study compares the performance of various machine learning
models, and XGBoost emerges as the most promising model, demonstrating superior
accuracy in predicting moisture content based on factors like soil properties, cement
content, and ultrasonic pulse velocity (UPV). The study employs SHAP (SHapley Additive
exPlanations) to understand how these features influence the model’s predictions.
UPV is the most significant factor affecting predicted moisture content, followed
by cement content and soil properties like uniformity coefficient. Also, the study
explores the possibility of using a reduced set of features for moisture content prediction.
They demonstrate that a combination of UPV, cement content, and uniformity
coefficient can achieve good accuracy, highlighting the potential for practical applications
where obtaining all data points might be challenging.
