@article{Palomino Ojeda_Rosario Bocanegra_Quiñones Huatangari_2021, title={Determination of the Compressive Strength of Concrete Using Artificial Neural Network}, volume={11}, url={https://ojs.imeti.org/index.php/IJETI/article/view/7479}, DOI={10.46604/ijeti.2021.7479}, abstractNote={<p>The objective of the work is to estimate the compressive strength of concrete by means of the application of Artificial Neural Networks (ANNs). A database is created with design variables of mixtures of 175, 210, and 280 kgf/cm², which are collected from certified laboratories of soil mechanics and concrete of the city of Jaen. In addition, Weka software is used for the selection of the variables and Matlab software is used for the learning, training, and validation stages of ANNs. Five ANNs are proposed to estimate the compressive strength of concrete at 7<sup>th</sup>, 14<sup>th</sup>, and 28<sup>th</sup> day. The results show that the networks obtain the average error of 4.69% and are composed of an input layer with eleven neurons, two hidden layers with nine neurons each, and the compressive strength of concrete as the output. This method is effective and valid for estimating the compressive strength of concrete as a non-destructive alternative for quality control in the construction industry.</p>}, number={3}, journal={International Journal of Engineering and Technology Innovation}, author={Palomino Ojeda, Jose Manuel and Rosario Bocanegra, Stefano and Quiñones Huatangari, Lenin}, year={2021}, month={Jun.}, pages={204-215} }