Abstract The use of statistical and machine learning approaches to predict the compressive strength of concrete based on mixture proportions, on account of its industrial importance, has received significant attention. However, previous studies have been limited to small, laboratory-produced data sets. This study presents the first analysis of a large data set (>10,000 observations) of measured compressive strengths from actual (job-site) mixtures and their corresponding actual mixture proportions. Predictive models are applied to examine relationships between the mixture design variables and strength, and to thereby develop an estimate of the (28-day) strength. These models are also applied to a laboratory-based data set of strength measurements published by Yeh et al. (1998) and the performance of the models across both data sets is compared. Furthermore, to illustrate the value of such models beyond simply strength prediction, they are used to design optimal concrete mixtures that minimize cost and embodied CO2 impact while satisfying imposed target strengths.

中文翻译：

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混凝土的抗压强度能否根据混合物比例的知识进行估计？：来自统计分析和机器学习方法的新见解

摘要 由于其工业重要性，使用统计和机器学习方法根据混合比例预测混凝土的抗压强度受到了极大的关注。但是，以前的研究仅限于实验室生成的小型数据集。本研究首次对来自实际（工地）混合物及其相应实际混合物比例的测量抗压强度的大型数据集（>10,000 次观察）进行了分析。应用预测模型来检查混合物设计变量和强度之间的关系，从而开发（28 天）强度的估计值。这些模型还应用于 Yeh 等人发表的基于实验室的强度测量数据集。(1998) 并比较了两个数据集上模型的性能。