## 3 Results of the Parametric Study  
In this parametric study, we have considered one linear meter of wall as a unitary reference value. Because of this, we have considered the variation of the main parameters in relation to the wall height for this unitary reference value. This study was performed for wall heights between 6 and 15 m with an increment of 1 m between them. The limits set for wall heights were set in accordance with those used in the article by [PERSON] et al. [44], reducing the values of heights to those whose results converged for the algorithm used for the optimization. The results between the energy and cost optimization were compared for different variables. Furthermore, we analyzed the influence of the concrete and reinforcing steel amounts for the two different objective functions. All the values gathered on the figures are the average values of the nine iterations carried out for each point. Figure 5 shows the trend of the values of cost, comparing energy and cost optimizations. As expected, energy optimization shows higher cost values compared with cost optimization. The embodied energy optimization objective function gives the values of cost adjusted to a parabolic curve, \(C=36.644\ H^{2}-299\ H+1834.1\), with a correlation coefficient of \(\mathbb{R}^{2}=0.9998\). In the same way, the embodied energy values obtained by embodied energy optimization adjust to \(E=176.15\ H^{2}-1845.9\ H+8916.5\), with a correlation coefficient of \(\mathbb{R}^{2}=0.9992\), as shown in Figure 6. The increase in the cost and the emissions related to the increase in the wall height are not linear and increase f  
Figure 4: Optimization process flowchart. As we can see in Figure 7, the trend of cost and energy are both linear if we compare energy with cost obtained for each optimization, this conducts us to the conclusion that both optimization objectives are linked. If we look at the slope of the cost optimization line, its value corresponds to 4.54, this means that a cost reduction of \(\ell\)1 produces a saving of 4.54 kWh of EE. Figure 8 displays the embodied energy associated with the amount of steel and concrete obtained by the embodied optimization process. As shown, the energy wasted by concrete is lower than steel from 6 to 9.5 m; from this height, the energy consumed by the concrete exceeds steel. This is due to the fact that concrete is capable of resisting the stresses up to a certain height of the wall, and after that point, it is necessary to largely increase the amount of steel to support the stresses. This energy consumption change is produced at the same time in the stem and foundation. The amount of concrete is always greater in the stem than in the foundation; however, if we focus on steel consumption, a change around 13.5 m of wall height can be observed, where the steel in the stem starts to be greater than the steel in the foundation. Figure 5: Relation between cost and wall height. Figure 6: Relation between embodied energy and wall height. It is important to obtain pre-dimensioning rules to obtain the reinforcement by each cubic meter of concrete and the volume of concrete used to build one meter of wall. In Figures 9 and 10, this data is shown for stem and footing due to the dependence of the results from the foundation on the characteristics of the terrain. As shown in Figure 9, the trend of the reinforcement ratio is parabolic to cost and embodied energy optimizations: the embodied energy optimization adjusts to \(R_{s}=-0.1853\;H^{2}+6.5067\;H-4.755\), with a R\({}^{2}=0.9781\) for the stem, and \(R_{f}=-0.3959\;H^{2}+13.978\;H-52.406\), with a R\({}^{2}=0.9403\) for the footing. The expression used to obtain the global reinforcement ratio for 1 m of wall is parabolic and adjusted to \(R=-0.2165\;H^{2}+8.502\;H-19.618\), with a R\({}^{2}=0.9818\). All adjustments have a good correlation coefficient, assuring a good prediction of the amount of reinforcing steel per cubic meter of concrete.