Modelling and Prediction of Concrete Compressive Strength Using Machine Learning

Abstract

The compressive strength of concrete plays an important role in determining the durability and performance of concrete. Due to rapid growth in material engineering finalizing an appropriate proportion for the mix of concrete to obtain the desired compressive strength of concrete has become cumbersome and a laborious task further the problem becomes more complex to obtain a rational relation between the concrete materials used to the strength obtained. The development in computational methods can be used to obtain a rational relation between the materials used and the compressive strength using machine learning techniques which reduces the influence of outliers and all unwanted variables influence in the determination of compressive strength. In this paper basic machine learning technics Multilayer perceptron neural network (MLP), Support Vector Machines (SVM), linear regressions (LR) and Classification and Regression Tree (CART), have been used to develop a model for determining the compressive strength for two different set of data (ingredients). Among all technics used the SVM provides a better results in comparison to other, but comprehensively the SVM cannot be a universal model because many recent literatures have proved that such models need more data and also the dynamicity of the attributes involved play an important role in determining the efficacy of the model.