Despite previous efforts to map the proportioning of a concrete to its strength, a robust knowledge-based 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.

尽管先前做出了努力来将混凝土的比例映射到其强度，但仍然缺少能够进行准确强度预测的可靠的基于知识的模型。作为基于物理或化学模型的替代方法，数据驱动的机器学习方法提供了解决该问题的有前途的途径。尽管机器学习可以推断出混凝土混合物比例和强度之间的复杂，非线性，非加性关系，但仍需要大型数据集来稳健地训练此类模型。这是一个令人担忧的问题，因为可靠的混凝土强度数据相当有限，尤其是对于实际的工业混凝土而言。在此，根据对来自工业混凝土的测量抗压强度的相当大的数据集（> 10,000个观测值）的分析，我们比较了三种选定的机器学习算法（多项式回归，人工神经网络（随机森林），以可靠地预测混凝土强度与训练数据集大小的关系。此外，通过采用分层抽样，我们研究了训练数据点的代表性对此处考虑的模型的学习能力的影响。基于这些结果，我们讨论了最终的精确度（在大型数据集上训练时）与实际需要多少数据来训练该模型之间的竞争性质。