Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods. (January 2019)

• Record Type: Journal Article
• Authors: Young, Benjamin A.; Hall, Alex; Pilon, Laurent; Gupta, Puneet; Sant, Gaurav
• Is Part Of: Cement and concrete research. Volume 115(2019)
• Page Start: 379
• Page End: 388
• Publication Date: 2019-01
• Subjects: Strength -- Mixture design -- Predictive modeling -- Machine learning -- Regression analysis; Cement -- Periodicals; Cement -- Research -- Periodicals; Concrete -- Periodicals; Concrete -- Research -- Periodicals; Ciment -- Périodiques; Béton -- Périodiques; Cement; Concrete; Periodicals; 620.135
• DOI: 10.1016/j.cemconres.2018.09.006
• Languages: English
• ISSNs: 0008-8846

Abstract

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.