## 4 Discussion  
### Model Optimization  
After finding the ideal models for each of the self-compacting mortar variables, through a central composite design, the model was optimized in order to find the best solutions based on certain criteria. Targets were set for each input and output variable, thereby encompassing maximization, minimization, achieving specific values, or falling within delimited intervals. The established limits for each variable were based on the database comprising 30 mixtures. In addition, weights ranging from 0.1 to 10 were defined (with the default being 1), along with a degree of importance ranging from 1 plus (+) to 5 plus (++++). This approach adheres to the methodology proposed by [PERSON], [PERSON], and [PERSON] [42], who aimed to maximize global desirability across a set of output variables. This study sought to identify mortar compositions that met the following criteria: (i) developed a higher compressive strength at 28 days with the lowest possible cement content to reduce production costs and environmental impacts and (ii) featured a high workability in order to facilitate the flow and ease of consolidation of the mixtures in slender structures and/or in highly reinforced arrangements. Table 12 presents the criteria for optimizing high-strength self-compacting mortars. For the input variable A (w/c), the objective was to minimize cement consumption; hence, a maximization goal was defined, with a weight of one and a maximum importance (factor 5 =++++). For the input variable C (w/p), the objective was to maximize the water content over the powder content, and it was also assigned the same importance as factor A but with a weight of one. As for the variable D (s/m), maximizing it allowed for reducing the consumption of mortar in relation to sand, thus further contributing to reducing costs and the environmental impacts. To increase the flowability of the self-compacting mortars and to optimize their performance during the concreting of slender elements with high reinforcement rates, the response variable Y1 (D-Flow) needed to be maximized and was ascribed a degree of importance of four. However, the maximum degree of importance was attributed to variable Y4, thereby aiming to maximize the compressive strength after 28 days with a minimum acceptance criterion of 110 MPa. The establishment of a minimum compressive strength criterion was envisioned to attain values surpassing the sample's average. Variables B (Sp/p), Y2 (T-Funnel), and Y3 (compressive strength after 24 h) were deemed to remain within acceptable value ranges, with a moderate degree of importance being attributed. After computing, a comprehensive set of 85 candidate solutions was found. Table 13 summarizes the top five optimized mixtures ranked based on their global desirability scores. Figure 7: Graphs for CS28d model: (**a**) Normal Plot of Residuals and (**b**) Predicted versus Actual. Figure 8 shows the desirability ramps of the different factors and responses for the candidate solution 1. It is observed that the input factors A, C, and D can be maximized without compromising the main objective of increasing the output variable Y4 (CS28d). Variable Y1 (D-Flow) attained its highest value of 343.3 mm, yet it still remained within the specified range of 282 to 377 mm. Factor B and output variables Y2 and Y3 adhered to the designated intervals, thereby aligning with the predefined criteria.