所蔵: 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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主要な著者:	Ziyad Sami B.H., Ziyad Sami B.F., Kumar P., Ahmed A.N., Amieghemen G.E., Sherif M.M., El-Shafie A.
その他の著者:	57481263600
フォーマット:	論文
出版事項:	Elsevier Ltd 2024
主題:	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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