Prediction of Concrete Compressive Strength Using Artificial Neural Network

Abstract

Concrete is the most widely used material by humans after water. Rapid growth in the construction industry, concrete will continue to be the dominant material in the future. Concrete is a composite material like aggregates, water, and admixtures. Destructive testing of concrete to know its strength achieved after the mix design will be an expensive and time-consuming process. With recent advances in soft computing techniques like artificial intelligence, these results can be predicted by feeding the algorithm with a large number of data available to obtain the desired results. In the present research work, it is proposed to use artificial neural networks to predict the strength of different types of concrete. A Multilayer Perceptron has input and output layers, and one or more hidden layers with many neurons stacked together. Data capturing will be done regarding different types of concrete and artificial neural networks are preliminarily trained with various inputs to solve problems with data applica- ble to obtain the desired results. This ANN with captured data helps in minimizing repetitive process and tests involved to obtain the results through experimental procedures which is time, material, and money- consuming with practical difficulties. The advantage of python is that designer can create a customized program for interactive design, Python determination also improve the analytical skill of the student and programs can be converted into executable software. Concrete Cubes are cast to validate the predicted Result of the Software.