## 4 Conclusions  
In this study, four Machine Learning (ML) algorithms for predicting the mechanical properties, more specifically the elastic Young's modulus (\(\text{E}_{\text{u}.50}\)), of soil-binder-water mixtures reinforced with fibres, were introduced and investigated.  
The four ML algorithms applied were Artificial Neural Networks (ANNs), Random Forest (RF), Support Vector Machines (SVMs) and Multiple Regression (MR), this last one being used for baseline comparison. Training and testing of the proposed models were performed on a representative data set of 121 records containing the 16 mechanical properties needed for the prediction of \(\text{E}_{\text{u}.50}\). The results showed an elastic modulus prediction accuracy with \(\text{R}^{2}\geq 0.93\) primarily for ANN and RF.  
A subsequent sensitivity analysis based on relative importance revealed some of the most important variables for \(\text{E}_{\text{u}.50}\). Binder dosage (\(D_{\text{bg/m}}\)\({}^{3}\)), fibre content (\(T_{\text{fibre}}\)), water content, water/cement content ratio, curing time, and clay content were found to be the most important in predicting \(\text{E}_{\text{u}.50}\).  
In summary, the ML algorithms have shown that they can be used to estimate specific mechanical properties of soil-cement mixtures reinforced with fibres if only data are available. This can help engineers in the design phase when testing is not possible or available budget is limited. In addition, these ML algorithms have proven their merits in complex geotechnical problems.  
Figure 5: Relative Importance for ML algorithms to indicate the most contributing variables to the elastic modulus (\(\text{E}_{\text{u}.50}\)) prediction.  
**Author Contributions:** Conceptualization, D.O.-A., J.T., A.-A.S.C. and P.J.V.O.; Software, J.T.; Investigation, D.O.-A., J.T., A.A.S.C. and P.J.V.O.; Methodology, D.O.-A., J.T., A.A.S.C. and P.J.V.O.; Validation, D.O.-A., J.T., A.A.S.C. and P.J.V.O.; Writing--original draft, D.O.-A.; Writing--review and editing, D.O.-A., J.T., A.A.S.C. and P.J.V.O.; Project administration, P.J.V.O.; Funding acquisition, A.A.S.C. and P.J.V.O. All authors have contributed to all parts of the work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Engineering Structures (ISISE), under reference UIDB/04029/2020, the R&D Unit Chemical Process Engineering and Forest Products Research Centre (CIFQPF) under reference UIDB/00102/2020, and under the project PTDC/ECICON/28382/2017.  
**Insituual Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in the present study are under privacy issues and cannot be shared.  
**Acknowledgments:** This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Engineering Structures (ISISE), under reference UIDB/04029/2020, and the R&D Unit Chemical Process Engineering and Forest Products Research Centre (CIFQPF) under reference UIDB/00102/2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.