Data Analytics Techniques for Concrete Compressive Strength Prediction - Shaswat, Kumar
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This Research Work incorporates different Data Analytic Techniques for a comparative study in predicting concrete strength aimed to improve the accuracy of prediction. The methods used are the linear regression with independent variable water to cement ratio (w/c), multi-linear regression with independent variables water/cement ratio, water, cement, blast furnace slag, fly ash, superplasticizers, fine aggregate, coarse aggregate, and age of testing, Regression Trees for better classification and prediction accuracy.

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Produktbeschreibung
This Research Work incorporates different Data Analytic Techniques for a comparative study in predicting concrete strength aimed to improve the accuracy of prediction. The methods used are the linear regression with independent variable water to cement ratio (w/c), multi-linear regression with independent variables water/cement ratio, water, cement, blast furnace slag, fly ash, superplasticizers, fine aggregate, coarse aggregate, and age of testing, Regression Trees for better classification and prediction accuracy.
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Autorenporträt
Shaswat, Kumar
A Civil Engineer, currently pursuing PhD from Bennett University. Working in the field of Deep Learning Application in Civil Engineering and Structural Health Monitoring.