Open AccessEstimating the flexural strength of concrete using compressive strength as input value in a deep learning model
Author(s) -
José Alberto Guzmán-Torres,
Francisco J. Domínguez-Mota,
Elia Mercedes Alonso-Guzmán
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1150/1/012019
Subject(s) - flexural strength , compressive strength , artificial neural network , sigmoid function , computer science , multilayer perceptron , structural engineering , artificial intelligence , materials science , engineering , composite material
The flexural strength is a critical parameter for designing many concrete structures such as rigid pavements, beams, and bridges. The standard test for concrete is the compressive strength due to its ease of implementation. There are many proposed methods for estimating flexural strength values with enough accuracy, although it is necessary to enhance the accuracy for this estimation, and this research suggests the use of artificial intelligence methods to accomplish this goal. Artificial Intelligence has been one of the most efficient approaches for estimating material parameters because of its efficient performance. This research presents the development of a data-driven Deep Neural Network for predicting the flexural strength in concrete based on just the compressive strength test. The proposed model analyses a concrete mixture with starch and a fluidizer. The model employs a Rectified Linear Unit function and a Sigmoid function in its architecture as activation functions and a considerable perceptron’s number. Results from the analysis show an excellent accuracy of over 90%, which is remarkable. This approach showed satisfactory performance in flexural strength prediction for the analysed concrete mixture.