Utilizing Multivariate Adaptive Regression Splines (MARS) for Precise Estimation of Soil Compaction Parameters

Abstract

Traditional laboratory methods for estimating soil compaction parameters, such as the Proctor test, have been recognized as time-consuming and labor-intensive. Given the increasing need for the rapid and accurate estimation of soil compaction parameters for a range of geotechnical applications, the application of machine learning models offers a promising alternative. This study focuses on employing the multivariate adaptive regression splines (MARS) model algorithm, a machine learning method that presents a significant advantage over other models through generating human-understandable piecewise linear equations. The MARS model was trained and tested on a comprehensive dataset to predict essential soil compaction parameters, including optimum water content (wopt) and maximum dry density (ρdmax). The performance of the model was evaluated using coefficient of determination (R2) and root mean square error (RMSE) values. Remarkably, the MARS models showed excellent predictive ability with high R2 and low RMSE, MAE, and relative error values, indicating its robustness and reliability in predicting soil compaction parameters. Through rigorous five-fold cross-validation, the model’s predictions for wopt returned an RMSE of 1.948%, an R2 of 0.893, and an MAE of 1.498%. For ρdmax, the results showcased an RMSE of 0.064 Mg/m3, an R2 of 0.899, and an MAE of 0.050 Mg/m3. When evaluated on unseen data, the model’s performance for wopt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R2 of 0.948. Similarly, for ρdmax, the predictions were characterized by an MAE of 0.047 Mg/m3, RMSE of 0.062 Mg/m3, and R2 of 0.919. The results also indicated that the MARS model outperformed previously developed machine learning models, suggesting its potential to replace conventional testing methods. The successful application of the MARS model could revolutionize the geotechnical field through providing quick and reliable predictions of soil compaction parameters, improving efficiency for construction projects. Lastly, a variable importance analysis was performed on the model to assess how input variables affect its outcomes. It was found that fine content (Cf) and plastic limit (PL) have the greatest impact on compaction parameters.

Keywords

• multivariate adaptive regression splines
• soil compaction
• optimum water content
• maximum dry density
• machine learning