Using machine learning to predict concrete's strength: learning from small datasets

Despite previous efforts to map the proportioning of a concrete to its strength, a robust knowledgebased model enabling accurate strength predictions is still lacking. As an alternative to physical or chemical-based models, data-driven machine learning methods offer a promising pathway to address this problem. Although machine learning can infer the complex, non-linear, non-additive relationship between concrete mixture proportions and strength, large datasets are needed to robustly train such models. This is a concern as reliable concrete strength data is rather limited, especially for realistic industrial concretes. Here, based on the analysis of a fairly large dataset (> 10,000 observations) of measured compressive strengths from industrial concretes, we compare the ability of three selected machine learning algorithms (polynomial regression, artificial neural network, random forest) to reliably predict concrete strength as a function of the size of the training dataset. In addition, by adopting stratified sampling, we investigate the influence of the representativeness of the training datapoints on the learning capability of the models considered herein. Based on these results, we discuss the nature of the competition between how accurate a given model can eventually be (when trained on a large dataset) and how much data is actually required to train this model.