Feasibility analysis for predicting the compressive and tensile strength of concrete using machine learning algorithms

• Main Authors: Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.
• Other Authors: 57481263600
• Format: Article
• Published: Elsevier Ltd 2024
• Subjects: Compressive strength; Concrete; Machine learning; Predictive modeling; Tensile strength; Bridges; Carbon footprint; Concrete aggregates; Concrete mixtures; Forecasting; High performance concrete; Learning algorithms; Matrix algebra; Regression analysis; Structural design; Tensile testing; Compressive strength of concrete; Gaussian process regression; Input variables; Machine learning algorithms; Machine-learning; Optimisations; Performance; Predictive models; Regression modelling

Concrete is the most utilized material (i.e., average production of 2 billion tons per year) for the construction of buildings, bridges, roads, dams, and several other important infrastructures. The strength and durability of these structures largely depend on the compressive strength of the concret...