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 (w_opt) and maximum dry density (ρ_dmax). 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 w_opt returned an RMSE of 1.948%, an R² of 0.893, and an MAE of 1.498%. For ρ_dmax, 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 w_opt prediction was marked with an MAE of 1.276%, RMSE of 1.577%, and R² of 0.948. Similarly, for ρ_dmax, 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 (C_f) and plastic limit (PL) have the greatest impact on compaction parameters.

This research investigated the feasibility of using waste-expanded polystyrene (EPS) and mortar to produce lightweight aggregate (LWA). The EPS extracted from the leftover backing waste was crushed into beads using an electric grinder and mixed in three different proportions with two types of mortars (with and without a superplasticizer). Physical property tests such as loose bulk density and water absorption were carried out for the LWA. Also, the compressive strength of the manufactured lightweight aggregate concrete (LWAC) was determined, and failure modes were discussed. The results indicate that using the EPS is effective for LWA. The loose bulk density is obtained at a range from 588 to 790 kg/m 3 , which meets the requirements of the American society for testing and materials C330 specification of LWA. For water absorption, the value obtained ranges from 6.45 to 14.05%, slightly higher than the normal aggregate due to the voids in the LWA. When using LWA containing a superplasticizer to produce LWAC, the compressive strength was higher than the concrete with LWA without a superplasticizer. The highest compressive strength for LWAC was 21 MPa.