Title: Predicting the Compressive Strength Based in NDT Using Deep Learning
Author(s): José A. Guzmán-Torres, Francisco J. Domínguez-Mota, Gerardo Tinoco-Guerrero, Elia M. Alonso-Guzmán, and Wilfrido Martínez-Molina
Publication: Symposium Paper
Appears on pages(s): 90-102
Keywords: concrete; compressive strength; deep learning; data science
Artificial Intelligence has one of the most efficient methods for solving engineering and materials problems because of its impressive performance and can reach higher accuracy. The Deep Learning theory is an approach based on Deep Neural Networks for establishing numerical analysis and value predictions. This paper proposes a fresh approach, using a Deep Learning model for predicting the compressive strength in a particular concrete just based on non-destructive test measurements (NDTs). The model proposed is an attractive alternative to estimate the resistance of compressive strength in any structure, just taking data like ultrasonic pulse velocity, electrical resistivity, and resonance frequencies. The present work employs data science techniques to find the correlation values between the NDTs and the compressive strength effort and realized broad numerical exploration about concrete performance. An amount of 285 specimens of concrete were monitored during this research. The model proposed contains 600 neurons and uses a Rectified Linear Unit and Sigmoid as activation functions where the NDTs were established as the input data. The dataset was segmented into two groups: train and test. In order to evaluate the model, the authors tested it in a validation set with different concrete features, achieving an accuracy of 94%.