Evaluation of the statically indeterminate form of continuous beams by the method of complex rotational flexibilities

1997 ◽  
Vol 33 (6) ◽  
pp. 607-613
Author(s):  
V. M. Baranovskii ◽  
A. I. Mil’chenko
Keyword(s):  
Continuous Beams ◽  
Indeterminate Form

Calculation of continuous beams taking into account the elastic compliance of supports

2018 ◽  
Vol 92 (4) ◽  
pp. 139-147
Author(s):  
Zh.S. Nuguzhinov ◽  
◽  
S.K. Akhmediev ◽  
S.R. Zholmagambetov ◽  
O. Khabidolda ◽  
...  
Keyword(s):  
Elastic Compliance ◽  
Continuous Beams

scholarly journals Neural Networks—Deflection Prediction of Continuous Beams with GFRP Reinforcement

2021 ◽  
Vol 11 (8) ◽  
pp. 3429
Author(s):  
Željka Beljkaš ◽  
Nikola Baša
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Research ◽  
Fiber Reinforced Polymer ◽  
Glass Fiber Reinforced Polymer ◽  
Reinforced Polymer ◽  
Continuous Beams ◽  
Glass Fiber Reinforced ◽  
Gfrp Reinforcement

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.

Study on the stiffness degradation in concrete continuous beam with externally prestressed CFRP tendons under fatigue loading

2021 ◽  
pp. 136943322199249
Author(s):  
Xing Li ◽  
Jiwen Zhang ◽  
Jun Cheng
Keyword(s):  
Fatigue Life ◽  
Fatigue Loading ◽  
Load Level ◽  
Fiber Reinforced Polymer ◽  
Stiffness Degradation ◽  
Test Results ◽  
Reinforced Polymer ◽  
Continuous Beams ◽  
Practical Engineering ◽  
Good Agreement

This paper presents fatigue behaviors and the stiffness degradation law of concrete continuous beams with external prestressed carbon fiber-reinforced polymer (CFRP) tendons. Three specimens were tested under fatigue loading, and the influence of different load levels on the stiffness degradation and fatigue life were studied, and it was found that the stiffness degradation of three test specimens exhibited a three-stage change rule, namely rapid decrease, stable degradation, and sharp decline, but there are obvious differences in the rate and amplitude of stiffness degradation. The load level has a significant influence on the fatigue life of the test specimens. An analytical model with load level considered was proposed to calculate the residual stiffness and predict the stiffness degradation, which is in good agreement with the test results. The model of stiffness degradation presents a possible solution for practical engineering applications of concrete continuous beams with externally prestressed CFRP tendons subjected to different fatigue loadings.

VIBRATIONS OF NON-UNIFORM CONTINUOUS BEAMS UNDER MOVING LOADS

2002 ◽  
Vol 254 (5) ◽  
pp. 911-926 ◽  
Author(s):  
Y.A. DUGUSH ◽  
M. EISENBERGER
Keyword(s):  
Moving Loads ◽  
Continuous Beams
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