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

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

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...

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

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