Machine Learning the Concrete Compressive Strength From Mixture Proportions

Abstract Concrete mixture design usually requires labor-intensive and time-consuming work, which involves a significant amount of “trial batching” approaches. Recently, statistical machine learning methods have demonstrated that robust model might help reduce the experimental work greatly. Here, we develop Gaussian process regression to shed light on relationship among contents cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine concrete compressive strength (CCS) at 28 days. A total 399 mixtures with CCS ranging from 8.54 MPa 62.94 are examined. The modeling approach is highly stable accurate, achieving correlation coefficient, mean absolute error, root square error 99.85%, 0.3769 (1.09% average CCS), 0.6755 (1.96% respectively. contributes fast low-cost estimations.