Stratified Metamodeling to Predict Concrete Compressive Strength Using an Optimized Dual-Layered Architectural Framework

Concrete is one of the most commonly used construction materials worldwide, and its compressive strength is the most important mechanical property to be defined at the time of structural design. Establishing a relationship between the amount of each component in the mixture and the properties of the concrete is not a trivial task, since a high degree of nonlinearity is involved. However, the use of machine learning methods as modeling tools has assisted in overcoming this difficulty. The objective of this work is to investigate the efficiency of using stacking as a technique for predicting the compressive strength of concrete mixtures. Four datasets obtained from the literature were used to verify the generalization capacity of the stacking technique; these datasets included a number of samples and numbers and types of attributes. Statistical tests were used to compare the existence of significant similarities between stacking and individual machine learning models. The results obtained from the statistical tests and evaluation metrics show that stacking yields results similar to those of the standalone machine learning models, with better performance.