Concrete Compressive Strength Machine Learning

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Concrete compressive strength machine learning. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. In this work three models are designed implemented and tested to determine the compressive strength of concrete. Predicting concrete compressive strength is just one application of the regression function of ml. This is generally determined by a standard crushing test on a concrete cylinder.

Ml is a branch of ai and can be used for several objectives e g classification regression clustering etc. The sequential model will be trained using the concrete compressive strength data set to learn to predict the compressive strength of concrete samples based on the material used to make them. A comparative analysis for the prediction of compressive strength of concrete at the ages of 28 56 and 91 days has been carried out using machine learning techniques via r software environment. Name data type measurement description cement component 1 quantitative kg in a m3 mixture input variable.

Machine learning methods have been successfully applied to many engineering disciplines. The compressive strength of high performance concrete hpc is a major civil engineering problem. The compressive strength of concrete is a highly nonlinear function of ingredients used in making it and their characteristics. Random forest svm and anns.

The concrete compressive strength is the regression problem. Artificial neural network ann and support vector machine svm models are generally used to resolve engineering problems. Please click on links below for more details. This requires engineers to build small concrete cylinders with different combinations of raw materials and test these cylinders for strength variations with a change in each raw material.

Thus using machine learning to predict the strength could be useful in generating a combination of ingredients which result in high strength. The compressive strength of concrete is a highly nonlinear function of ingredients used in making it and their characteristics. Please read this blog for more explanation. On the other hand with the development of artificial intelligence ai in recent years it is a trend to use machine learning ml techniques to predict the concrete compressive strength.

The compressive strength of concrete determines the quality of concrete. Thus using machine learning to predict the strength could be useful in generating a combination of ingredients which result in high strength.