Abstract
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.
Highlights
The most commonly used material for reinforcing reinforced concrete (RC) elements in the construction of building structures is steel
Steel reinforcement is subject to corrosion that causes damage to the concrete and endangers the functionality and usability of reinforced concrete structures
Corrosion prevention costs can be significant. This fact is important in reinforced concrete structures that are exposed to the aggressive action of the environment
Summary
The most commonly used material for reinforcing reinforced concrete (RC) elements in the construction of building structures is steel. Deflection prediction for continuous beams can be performed by applying one of the artificial intelligence methods, artificial neural networks. Predictions through the application of artificial neural networks are performed in all areas of construction.
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