scholarly journals Discussion: Experimental research on plastic design method and moment redistribution in continuous beams prestressed with unbonded tendons

2011 ◽  
Vol 63 (10) ◽  
pp. 783-784
Author(s):  
Wei Zhou ◽  
WenZhong Zheng ◽  
Bernard Espion
Keyword(s):  
Experimental Research ◽  
Design Method ◽  
Plastic Design ◽  
Moment Redistribution ◽  
Continuous Beams ◽  
Unbonded Tendons

scholarly journals Experimental Research of Continuous Concrete Beams with GFRP Reinforcement

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nikola Baša ◽  
Mladen Ulićević ◽  
Radomir Zejak
Keyword(s):  
Experimental Research ◽  
American Concrete Institute ◽  
Rc Structures ◽  
Moment Redistribution ◽  
Continuous Beams ◽  
Load Carrying ◽  
Current Design ◽  
Frp Reinforcement ◽  
Internal Forces ◽  
Gfrp Reinforcement

Continuous beams are often used within RC structures, which are exposed to aggressive environmental impact. The use of the fiber-reinforced polymer (FRP) reinforcement in these objects and environments has a big significance, taking into account tendency of steel reinforcement to corrode. The main aim of these research studies is to estimate ability of continuous beams with glass FRP (GFRP) reinforcement to redistribute internal forces, as a certain way of ductility and desirable behaviour of RC structures. This paper gives the results of experimental research of seven continuous beams, over two spans of 1850 mm length, cross-section of 150 × 250 mm, that are imposed to concentrated forces in the middle of spans until failure. Six beams were reinforced with different longitudinal GFRP and same transverse GFRP reinforcements, and one steel-reinforced beam was adopted as a control beam. The main varied parameters represent the type of GFRP reinforcement and ratio of longitudinal reinforcement at the midspan and at the middle support, i.e., design moment redistribution. The results of the research have shown that moment redistribution in continuous beams of GFRP reinforcement is possible, without decreasing the load-carrying capacity, compared to elastic analysis. The test results have also been compared to current code provisions, and they have shown that the American Concrete Institute (ACI) 440.1R-15 well predicted the failure load for continuous beams with GFRP reinforcement. On the contrary, current design codes underestimate deflection of continuous beams with GFRP reinforcement, especially for higher load levels. Consequently, a modified model for calculation of deflection is proposed.

A new simple plastic design method for continuous beams

1973 ◽  
Vol 8 (4) ◽  
pp. 373-377
Author(s):  
B.V. Subrahmanyam ◽  
P.Srinivasa Rao
Keyword(s):  
Design Method ◽  
Plastic Design ◽  
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.

Minimum Weight Plastic Design of Continuous Beams

1981 ◽  
Vol 107 (7) ◽  
pp. 1263-1277
Author(s):  
Yuhshi Fukumoto ◽  
Mitsuru Ito
Keyword(s):  
Minimum Weight ◽  
Plastic Design ◽  
Continuous Beams
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