Next Article in Journal
Constructed Technosols: A Strategy toward a Circular Economy
Previous Article in Journal
Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems
Article

Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement

Faculty of Civil Engineering, University of Montenegro, 81000 Podgorica, Montenegro
*
Author to whom correspondence should be addressed.
Academic Editor: Chilukuri K. Mohan
Appl. Sci. 2021, 11(8), 3429; https://doi.org/10.3390/app11083429
Received: 27 February 2021 / Revised: 26 March 2021 / Accepted: 30 March 2021 / Published: 12 April 2021
(This article belongs to the Section Civil Engineering)
Deflections on continuous beams with glass fiber-reinforced polymer (GFRP) reinforcement are calculated in accordance with the appropriate standards (ACI 440.1R-15, CSA S806-12). However, experimental research provides results which differ from the values calculated pursuant to the standards, particularly when it comes to continuous beams. Machine learning methods can be applied for predicting a deflection level on continuous beams with GFRP (glass fiber-reinforced polymer) reinforcement and loaded with a concentrated load. This paper presents research on using artificial neural networks for deflection estimation and an optimal prediction model choice. It was necessary to first develop a database, in order to train the neural network. The database was formed based on the results of the experimental research on continuous beams with GFRP reinforcement. Using the best trained neural network model, high accuracy was obtained in estimating deflection, expressed over the mean absolute percentage error, 9.0%. This result indicates a high level of reliability in the prediction of deflection with the help of artificial neural networks. View Full-Text
Keywords: artificial intelligence; neural networks; prediction; deflection; continuous beam; GFRP bars artificial intelligence; neural networks; prediction; deflection; continuous beam; GFRP bars
Show Figures

Figure 1

MDPI and ACS Style

Beljkaš, Ž.; Baša, N. Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement. Appl. Sci. 2021, 11, 3429. https://doi.org/10.3390/app11083429

AMA Style

Beljkaš Ž, Baša N. Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement. Applied Sciences. 2021; 11(8):3429. https://doi.org/10.3390/app11083429

Chicago/Turabian Style

Beljkaš, Željka, and Nikola Baša. 2021. "Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement" Applied Sciences 11, no. 8: 3429. https://doi.org/10.3390/app11083429

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop