Predicting the Concrete Properties Using Machine Learning A Step Towards Smart Infrastructure

Sana Shabir Khan; Anuj Sachar; Sandeep SinglA

doi:10.55524/

Authors

Sana Shabir Khan M.Tech Scholar, Department of Civil Engineering, RIMT University, Mandigobindgarh, Punjab, India Author
Anuj Sachar Assistant Professor, Department of Civil Engineering, RIMT University, Mandigobindgarh, Punjab, India Author
Sandeep SinglA Professor, Department of Civil Engineering, RIMIT University, Mandigobindgarh, Punjab, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Machine Learning (ML), Root mean square error (RMSE), Compressive Strength, Multivalued Linear Regression (MVLR), Artificial Neural Network (ANN), Decision Trees

Abstract

The mechanical properties of concrete mixtures are of a great concern when engineers need to provide the estimation of the concrete strength in addition to forecast the behavior of innovative concrete types. Predicting such mechanical properties like compressive strength, shear strength, tensile strength, and elastic modulus of concrete etc has motivated researchers to pursue reliable models for predicting mechanical strength. Empirical and statistical models, such as linear and nonlinear regression, have been widely used. However, these models require laborious experimental work to develop, and can provide inaccurate results when the relationships between concrete properties and mixture composition and curing conditions are complex. To overcome such drawbacks, several Machine Learning models have been implemented as an alternative approach for predicting the mechanical strength of concrete. The present study reviews ML models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, etc. The application of each model and its performance has been discussed and analyzed.

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