## 1 Introduction  
Concrete is one of the main elements used in civil construction, due to its variability in use, which only intensifies with urban development. However, concrete production heavily relies on natural resource consumption, including the fundamental constituents for cement production and the extraction of coarse and fine aggregates, thereby causing deleterious environmental impacts [1, 2]. Self-compacting concrete (SCC) was developed to improve concrete fluidity and self- consolidation properties, thus obviating the requirement for external compaction elements while also fostering enhanced mechanical properties [3, 4, 5, 6]. When producing self-compacting cement-based products, it is recommended to reduce the proportion of coarse aggregates and to increase the amount of fine aggregates and cement to reduce the risk of segregation and increasing properties such as viscosity, void filling, and stability, in addition to mechanical properties [7]. However, such a mix design approach may be prone to cracking and shrinkage, in addition to increased production costs [5, 8]. The use of mineral and chemical additives in SCC aims to increase durability and workability while reducing production costs [9, 10]. Moreover, contemporary architecture demands slender structures of highly intricate and complex geometry, which increase the demand for innovative self-leveling mortars and concrete with a high strength development [10; 11; 12]. However, the design of high-strength cement-based materials with self-compacting attributes is challenges due to inherent contradictions in mixture design requirements. For instance, achieving a high early strength typically requires low water/binder ratios (w/b), which, conversely, reduce the mixture's self-compacting ability. Therefore, traditional mixed design approaches may prove inadequate when addressing the demanding requirements of self-compacting mortars. Data-driven mixed design methodologies may offer a promising alternative method to gain valuable insights on how to enhance the fresh properties of self-compacting mortars while maximize their strength development. Design of experiments (DoE) is a statistical tool that has been used in the optimization of materials, and its advantages include correlating the investigated variables, finding an optimal response within an investigation region, reducing the number of required experimental trials, and even defining optimized models according to predefined parameters [13; 14]. The response surface methodology (RSM) can be used to find relationships between input and output variables and to define the optimization criteria among variables [15; 16; 17; 18]. In recent decades, investigations using statistical tools in concrete and mortar mix design have been intensified. Research has applied the RSM to maximize the replacement of cement or aggregates with--among others--pumice stone [1], foundry sand [18], plastic waste and silica fume [19], and hybrid steel fibers [20]. The authors developed high-significance models, thereby making it possible to optimize the performance of cementitious mixtures while reducing the use of prime raw materials. Other studies were undertaken with the primary objective of improving the environmental and economic performance of cementitious materials [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. More recently, the use of statistical methodologies has gained attention in the realm of self-compacting cementitious materials. Its application extends to various domains, thus encompassing the assessments of rheological effects and mechanical properties, as well as the mitigation of early-stage cracking. [PERSON] et al. [39] employed regression models, utilizing central composite design (CCD), to examine the influence of four mixed design variables (fly ash content (FA), silica fume content (SF), sand-binder ratio (s/b), and water-binder ratio (w/b)) on the rheological and mechanical properties of concrete.