Span-to-depth ratio limits for RC continuous beams and slabs based on MC2010 and EC2 ductility and deflection requirements

2021 ◽  
Vol 228 ◽  
pp. 111565
Author(s):  
José Santos ◽  
António Abel Henriques
Keyword(s):  
2018 ◽  
Vol 92 (4) ◽  
pp. 139-147
Author(s):  
Zh.S. Nuguzhinov ◽  
◽  
S.K. Akhmediev ◽  
S.R. Zholmagambetov ◽  
O. Khabidolda ◽  
...  

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 445
Author(s):  
José Valdez Aguilar ◽  
César A. Juárez-Alvarado ◽  
José M. Mendoza-Rangel ◽  
Bernardo T. Terán-Torres

Concrete barely possesses tensile strength, and it is susceptible to cracking, which leads to a reduction of its service life. Consequently, it is significant to find a complementary material that helps alleviate these drawbacks. The aim of this research was to determine analytically and experimentally the effect of the addition of the steel fibers on the performance of the post-cracking stage on fiber-reinforced concrete, by studying four notch-to-depth ratios of 0, 0.08, 0.16, and 0.33. This was evaluated through 72 bending tests, using plain concrete (control) and fiber-reinforced concrete with volume fibers of 0.25% and 0.50%. Results showed that the specimens with a notch-to-depth ratio up to 0.33 are capable of attaining a hardening behavior. The study concludes that the increase in the dosage leads to an improvement in the residual performance, even though an increase in the notch-to-depth ratio has also occurred.


2021 ◽  
Vol 11 (8) ◽  
pp. 3429
Author(s):  
Željka Beljkaš ◽  
Nikola Baša

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.


2021 ◽  
pp. 136943322199249
Author(s):  
Xing Li ◽  
Jiwen Zhang ◽  
Jun Cheng

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.


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