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

• المؤلفون الرئيسيون: Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.
• مؤلفون آخرون: 57481263600
• التنسيق: مقال
• منشور في: Elsevier Ltd 2024
• الموضوعات: 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...