Application of machine learning techniques for predicting compressive, splitting tensile, and flexural strengths of concrete with metakaolin
The mechanical properties of concrete are the important parameters in a design code. The
amount of laboratory trial batches and experiments required to produce useful design data
can be decreased by using robust prediction models for the mechanical properties of
concrete, which can save time and money. Portland cement is frequently substituted with
metakaolin (MK) because of its technical and environmental advantages. In this study, three
mechanical properties of concrete with MK, ie, compressive strength (f′ c), splitting tensile …
amount of laboratory trial batches and experiments required to produce useful design data
can be decreased by using robust prediction models for the mechanical properties of
concrete, which can save time and money. Portland cement is frequently substituted with
metakaolin (MK) because of its technical and environmental advantages. In this study, three
mechanical properties of concrete with MK, ie, compressive strength (f′ c), splitting tensile …
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