## 4 Conclusions  
This work explored four Machine Learning (ML) algorithms to predict the mechanical properties of soil-binder-water mixtures reinforced with fibers. Thus, Artificial Neuronal Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF) and Multiple Regression (MR), which were used as a baseline comparison, were implemented to predict Unconfined Compressive Strength (UCS) and Indirect Tensile Strength (ITS) development. The proposed models, supported on representative databases comprising around 100 records each, were able to catch both mechanical properties behavior with a promising performance (R\({}^{2}\) higher than 0.95), particularly those based on ANNs. For that, sixteen variables covering information about the three main components involved in these types of mixtures (i.e., soil, fibers and binder) have been considered.  
By addressing a global sensitivity analysis, a deeper understanding of the proposed models was extracted, showing that the binder content is one the most influential variable in both UCS and ITS prediction. Moreover, it was observed that in the ITS study, the type and characteristics of the fibers are more relevant than in the UCS study, which corroborates some experimental findings.  
In conclusion, the proposed models can be used as an important tool for design purposes, allowing a very accurate estimation of the final properties of soil-binder-water mixtures reinforced with fibers by considering only information available without preparing/testing any sample. Moreover, it was shown once again the advantages of implementing a data-driven approach to explore complex geotechnical problems.  
Figure 8: A comparison of the relative importance of each input variable based on a GSA in ITS prediction of soil-binder-water mixtures reinforced with fibers.  
**Author Contributions:** Conceptualization, J.T., A.A.S.C. and P.J.V.O.; Software, J.T.; Investigation, J.T., A.A.S.C. and P.J.V.O.; Methodology, J.T., A.A.S.C. and P.J.V.O.; Validation, J.T., A.A.S.C. and P.J.V.O.; Writing--original draft, J.T.; Writing--review & editing, 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 for 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 (CIEPQPF) under reference UIDB/00102/2020, and under the project PTDC/ECL-CON/28382/2017.  
**Institutional 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 (CIEPQPF) under reference UIDB/00102/2020. A special thanks also to ACTV for the financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.