Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques

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Machine learning in concrete strength simulations: Multi-nation data analytics (2014, methodology): “In the literature, LR is commonly used for modeling the mechanical properties of construction materials [6,7,20,23,48].”
Computational design optimization of concrete mixtures: A review (2018, methodology): “However, if the goal is to design a more tailored or high-performing concrete (HPC), more decision variables will likely be included, such as aggregate size, superplasticizer dosage, and the amounts of fly ash, slag, or silica fume.”
Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods (2019, methodology): “Several other studies [11,12,18,26] have used the same dataset published by Yeh et al.”
Analysing the concrete compressive strength using Pearson and Spearman (2017, background): “The concrete strength varies with the applicability needs [1], [5], [12], [23], [26], and its effects vary with materials [3], [7], [9], [14], [16], [21], [25].”