## 6 Conclusions  
Determining compacted parameters in geotechnical projects is vital for ensuring soil stability, optimizing foundation designs, mitigating settlement issues, and guaranteeing the safety and longevity of structures built on or within the ground. For this reason, the prediction of those parameters is imperative for proactive planning, cost-effective execution, and minimizing project risks. This research aims to predict those parameters via employing the MARS (multivariate adaptive regression splines) model. Compared to other black-box machine learning models, the MARS model offers advantages such as flexibility in capturing non-linear relationships, automated interaction detection, fewer tuning parameters, and the capability to produce more interpretable results, making it particularly suitable for geotechnical applications where understanding the underlying behavior is as crucial as prediction accuracy.  
The results obtained through this research indicate the following:  
* Hyperparameter tuning of the MARS model significantly enhanced its predictive performance, reducing the error rate and refining the model's adaptability to complex geotechnical data.
* The final optimal models for parameters \(w_{opt}\) and \(\rho_{\rm{dmax}}\) were evaluated using five-fold cross-validation. The findings showed consistency and high accuracy across all folds, demonstrating the model's robustness and reliability in estimating these critical parameters.
* Testing the models on unseen data (testing dataset) revealed that they maintained a commendable level of precision and generalization, further substantiating their efficacy for real-world applications in geotechnical projects.
* Comparing the MARS model with other state-of-the-art models, such as Multi Expression Programming (MEP), indicated that while both models exhibit strong predictive capabilities, MARS demonstrated faster convergence and better handling of non-linear relationships, offering a more efficient and robust alternative for geotechnical parameter prediction.
* Finally, the variable importance analysis revealed that the fine content (C\({}_{\rm{F}}\)) and plasticity limit (PL) are the most influential factors driving the predictive accuracy of the model, highlighting their pivotal role in determining the geotechnical properties under study.  
It is worth mentioning that ML research often presents inherent challenges and limitations. In this study, a notable limitation is the data sample size of 226 points. The model formulated in this research delivers the anticipated accuracy when analogous input parameters are subsequently applied. However, errors may arise in the analysis if the same parameters are used but lie outside our established range. Gathering more experimental data in the future is essential to enhance the model's generalization capabilities. Upcoming studies will explore the potential of advanced ML techniques, such as deep learning, in predicting \(w_{opt}\) and \(\rho_{\rm{dmax}}\) values.  
**Author Contributions:** Writing--original draft, H.I.; Investigation, F.J.K.; Software, J.K.A. and M.S.A.; Data curation, S.N.H.; Methodology, F.J.K.; Supervision, M.S.A.; Funding acquisition, L.F.A.B.; Validation, J.K.A.; Writing--review and editing, H.I. and L.F.A.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.