## 2 Methodology
### Experiment and Data  
The soils used for the preparation of the laboratory samples were natural soils collected from the lower valley of the Mondego River and from a gravel and silt pit, ranging from cohesive to cohesionless soils, from organic to inorganic soils, and having different geotechnical properties. In all cases, the soils were chemically stabilised with Portland cement, the most used binder for soil stabilisation [42], which was used in varying amounts from 75 to 500 kg/m\({}^{3}\). Four fibres were selected for the laboratory experiments. The idea behind this selection is to integrate all the types of fibres usually employed in soil stabilisation. These are one natural fibre (Sisal) and three artificial fibres. The others, a synthetic (Polypropylene) and two metallic fibres (Dramix and Wiremix, varying the anchoring conditions of the fibres), which are characterised by different mechanical properties, namely stiffness and tensile strength. The length of the fibres varied from 12 to 30 mm, and they were used in different amounts from 2 to 150 kg/m\({}^{3}\). A detailed description of all materials can be found in [9; 17; 43]. In the case of the models, training and testing were performed on a dataset comprising 121 records for the [PERSON]'s modulus (\(\text{E}_{\text{u}.50}\)) studies. The samples were prepared in a controlled environment within a laboratory testing program developed at the University of Coimbra. The objective of this program was to characterise the mechanical property Young's modulus (\(\text{E}_{\text{u}.50}\)) of soil-binder-water mixtures reinforced with fibres through unconfined compressive strength tests. The parameters considered in the study were soil properties (grain size composition, organic matter content, water content, Atterberg limits), binder content, curing time, and fibre properties (changing origin, length, fibre content, mechanical properties [9; 14; 18]). A set of 16 variables were selected as input variables to train the algorithms. Of all the variables available in the study, these 16 features are considered to influence the behavior of mechanical properties, as can be seen in the literatures [35; 44; 45]. Moreover, they were also seen as pertinent by [4] in the UCS and ITS studies. The following are the 16 input variables and statistics (Table 1) for all variables in this study. Figure 2 shows the matrix scatter plots for each variable predicting the elastic modulus. The Pearson correlation above the diagonal examines the linear relationship between two continuous variables. The correlation coefficient ranges from \(-1\) to \(+1\), and the larger the absolute value the stronger the relationship between the variables. A positive and negative value indicates a positive and negative slope, respectively. A perfect linear relationship corresponds to an absolute value of 1, and a correlation of 0 indicates no linear relationship between variables. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Minimum** & **Maximum** & **Mean** & **Std. Deviation** \\ \hline \({}^{\circ}\)Sand & 14.00 & 100.00 & 36.41 & 33.23 \\ \({}^{\circ}\)Silt & 0.00 & 61.00 & 45.49 & 23.56 \\ \({}^{\circ}\)Clay & 0.00 & 25.00 & 18.10 & 10.09 \\ \({}^{\circ}\)OM & 0.00 & 13.05 & 8.01 & 5.12 \\ \({}^{\circ}\)WL\({}_{\rm{L}}\) & 0.00 & 80.00 & 54.68 &  
Therefore, a close examination of Figure 2 shows that of the 16 variables, some had good correlation: for instance, sand and silt, sand and clay, silt and clay and clay and soil organic matter (OM). However, although there were good correlations among some variables, all of them were considered as models attributes. An overall superior performance of the models was observed considering all variables. 1. Soil sand content (%)--%Sand
2. Soil silt content (%)--%Silt
3. Soil clay content (%)--%Clay
4. Soil organic matter content (%)--%OM
5. Liquid limit--WLL
6. Plastic limit--WPL
7. Water content (%)--W0
8. Cement content (%)--aw
9. Cement dosage (kg/m\({}^{3}\))-- D\({}_{\text{kg/m}^{3}}\)
10.