M. Bauchy, G. Sant
University of California, Los Angeles,
Keywords: concrete, machine learning, strength
Summary:The properties of cementitious binders are controlled by their composition and structure at different scales. However, the complexity of their disordered, multi-scale structure makes it challenging to elucidate such linkages. In particular, due to a lack of physical models, predicting the strength development of concretes remains challenging. As an alternative route to physics-based models, machine learning offers a promising pathway to develop new predictive models for materials based on existing datasets. Here, we show that machine learning techniques can be used to reliably predict concrete’s strength development. This approach relies on the analysis of a large data set (>10,000 observations) of measured compressive strengths from actual (job-site) mixtures and their corresponding actual mixture proportions. The developed model successfully predicts the 7-day and 28-day strength of concretes based on the mere knowledge of the mixture proportions with an accuracy of ±4.5 MPa. We illustrate how this approach can be used to identify optimal concrete mixtures with reduced cost and CO2 footprint while satisfying target strengths.