Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis

Abstract

The current study applies a soft-computing approach based on the gradient boosting method to predict the unconfined compressive strength (UCS) of sands treated with microbially-induced calcite precipitation (MICP). A 10-fold cross-validation method and hyperparameter tuning are performed to find the optimal architecture of the gradient boosting algorithm. A total of 402 data of unconfined compression tests performed on biocemented sands are utilized in this study. The dataset includes eight input parameters: median sand particle size, uniformity coefficient of sand, initial void ratio, calcium chloride concentration, urea concentration, urease activity, optical density of bacteria, and calcite content. The finding demonstrates that the gradient boosting method outperformed five commonly used machine learning algorithms (artificial neural networks, random forests, k-nearest neighbors, support vector regression, and decision trees) in predicting the UCS of biocemented sands. Using the gradient boosting, the predicted UCS has a strong correlation with the actual values (R2 = 0.95). Moreover, a series of correlation and feature importance analyses are carried out over the dataset. The relationships between unconfined compressive strength, calcite content, and initial void ratio are discussed within the article. Furthermore, some guidelines are provided for assessing the effect of environmental factors on the UCS of biocemented sands. For further study, the limitations of this study regarding the insufficiency of data for correlation and environmental modification are addressed.