Computational intelligence applied in the prediction of the compressive strength of Portland cement concrete
Concrete is one of the most widely used building materials, being composed of different components with different properties, which makes the task of dosing and strength determination complex. Artificial Neural Networks is a tool that has the ability to generalize and learn from previous experiences that are provided by a previously built database. This work aims the implementation of RNA in determining the compressive strength of concrete of various ages. The input data is the material quantities and the output is the compressive strength. The results obtained are promising and advantageous from the point of civil engineering, since the average correlation coefficient obtained was 0.96559, with the neural network showing agility and a low error rate in the inserted context, with an efficiency of approximately 95%.