###### Abstract  
The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes. concrete; compressive strength; flexural strength; slump; machine learning; optimization algorithms +
Footnote †: journal: Applied Sciences  
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Footnote 0: _Applied Sciences_  
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Footnote 0: _Applied Sciences_  
needs. Cement is a binding material with adhesive and cohesive properties that allow it to bond different mineral fragments to form a compact structure with adequate strength and durability [6]. Once it comes into contact with water, the hydration process begins, undergoing a series of chemical reactions that lead to the formation of calcium silicate hydrates (C-S-H). This gel gains strength over time, transforming it into a compound with the mechanical properties necessary for structural applications. Supplementary cementitious materials (SCMs) are alternative binders that allow the reduction of the amount of clinker used in blended cements or as cement substitutes in concrete production. Since the 1970s, quantifiable methods such as the cement factor have been developed [7], as well as more recent standards such as ASTM C618 [8], which allows the determination of the pozzolanic activity of fly ash or natural pozzolans for use in concrete, ensuring the correct performance of the mixes. Among these materials are fly ash (FA), natural pozzolans (NP) or artificial pozzolans (AP), silica fume (SF), and ground granulated blast furnace slag (GGBFS) [9]. Water is a component that enables the reaction in cement and imparts plasticity to concrete. The mixture of cementitious material and water is known as paste. During the hydration process, cement undergoes chemical reactions that give it the property of setting and hardening to form a solid material [6]. The proportion of the amount of water to the amount of cement is known as the water-to-cement ratio (w/c). This ratio represents one of the most determining and influential factors in concrete properties such as strength, slump, and durability. Fine and coarse aggregates occupy between 60% and 75% of the volume of concrete and influence its properties in both fresh and hardened states [10]. Fine aggregate refers to natural sands or crushed rock with particle sizes smaller than 4.75 mm, while coarse aggregate consists of rocks with particle sizes larger than 4.75 mm; both can be extracted directly from rock formations or deposits transported by rivers.