Published Paper Details:

The most fundamental input of the construction sector is concrete, which would be a massively complicated element. Concrete is among the most common structural construction materials due to its strength. Since some manufacturers manufacture out of reach and low quality, there is a growing demand for earthquake-resistant design in the fully prepared concrete industry. Concrete's strength-gaining properties are influenced by a variety of factors. This research aims to use the results of early compressive strength tests to predict strength properties at various ages. The ability to estimate the determination and strength of normal concrete using the early day strength properties result has been examined. Including both concrete and regional concrete mixes, a basic numerical equation forecast the concrete strength at any age is proposed. The goal of this article is to show how artificial neural networks (ANN) and machine learning can be used to forecast the compressive strength of high-performance concrete. On the other side, we'll evaluate the errors of all of the techniques we're using. The dataset used was obtained from the UCI Machine Learning repository. As a result of the research, it was discovered that the Random Forest Algorithm and Artificial Intelligent gives the best performance when all input parameters were used, including cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age.