Published in ‘Data-Centric Engineering’
A new paper from researchers at the University of Cambridge and Intellegens demonstrates the effectiveness of machine learning in predicting properties of potential lower-carbon concrete products. Such prediction could help to speed the development of new concrete formulations that lower the environmental impact of concrete – a major contributor to greenhouse gas emissions.
Citation Zviazhynski B, White C, Lees JM, Conduit GJ. Predicting concrete strength using machine learning and fresh-state measurements. Data-Centric Engineering. 2025;6:e37.
DOI 10.1017/dce.2025.10018
Abstract
Understanding the properties of lower-carbon concrete products is essential for their effective utilization. Insufficient
empirical test data hinders practical adoption of these emerging products, and a lack of training data limits the
effectiveness of current machine learning approaches for property prediction. This work employs a random forest
machine learning model combined with a just-in-time approach, utilizing newly available data throughout the
concrete lifecycle to enhance predictions of 28 and 56 day concrete strength. The machine learning hyperparameters
and inputs are optimized through a novel unified metric that combines prediction accuracy and uncertainty estimates
through the coefficient of determination and the distribution of uncertainty quality. This study concludes that
optimizing solely for accuracy selects a different model than optimizing with the proposed unified accuracy and
uncertainty metric. Experimental validation compares the 56-day strength of two previously unseen concrete mixes to
the machine learning predictions. Even with the sparse dataset, predictions of 56-day strength for the two mixes were
experimentally validated to within 90% confidence interval when using slump as an input and further improved by
using 28-day strength.
