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

By B. Young, A. Hall, Laurent Pilon, Puneet Gupta, Gaurav Sant. Cement and Concrete Research, 2019

388 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/a8d4f47b0eda2dfc84ffbe04aaa504128c194e30