Application and validation of internally cured concrete strength characteristics by machine learning

Abstract

Water is a precious resource, and the demand for potable water is high in many regions globally. Potable water is being used in the construction of buildings, and a substantial amount of water is required for curing concrete elements, which can be saved with modern construction practices like internal or self-curing concrete. The properties of concrete are usually identified through physical testing. Experiments become time-consuming, laborious and costly if multiple parameters in small proportions are to be modified and the results are to be predicted. Predicting nominal mix proportions becomes difficult; alternatively, utilising computational and predicting any specific or multiple outputs by analysing multiple input parameters with randomised data is feasible through machine learning. Considering this, an attempt is made to develop self-curing concrete of M30 grade utilising fractional replacement of 30% coarse aggregates with fly-ash aggregate as an internal curing agent. Different algorithms those that are processed in machine learning deal with prediction and validation of concrete characteristic strength parameters; 1) decision tree regression; 2) random forest regression, and 3) multiple layer perception regression analysis. In this study, the comparison found that the utmost successful machine learning strategy for prediction of concrete strength properties is the decision tree regressor method, with the least error fitting the regression curve.