STUDY ON CENTRIFUGAL REINFORCED POLYMER CONCRETE PIPE

Author(s):  
F Omata ◽  
H Tokushige ◽  
M Kawakami ◽  
O Shinoe ◽  
H Okamoto
2010 ◽  
Vol 168-170 ◽  
pp. 549-552
Author(s):  
Yan Lei Wang ◽  
Qing Duo Hao ◽  
Jin Ping Ou

A new form of fiber reinforced polymer (FRP)-concrete composite beam is proposed in this study. The proposed composite beam consists of a GFRP box beam combined with a thin layer of concrete in the compression zone. The interaction between the GFRP beam and the concrete was obtained by bonding coarse-sand on the top flange of the GFRP beam. One GFRP box beam and one GFRP-concrete composite beam were investigated in four-point bending test. Load-deflection response, mid-span longitudinal strain distributions and interface slip between GFRP beam and the concrete for the proposed composite beam were studied. Following conclusions are drawn from this study: (1) the stiffness and strength of the composite beam has been significantly increased, and the cost-to-stiffness ratio of the composite beam has been drastically reduced comparing with GFRP-only box beam; (2) a good composite action has been achieved between the GFRP beam and the concrete; (3) crushing of concrete in compression defines flexural collapse of the proposed composite beam..


Author(s):  
G Priniotakis ◽  
H Bouguessir ◽  
E Harkati ◽  
M Rokbi ◽  
S Vassilliadis

2013 ◽  
Vol 19 (2-3) ◽  
pp. 203-210
Author(s):  
H. N. Atahan ◽  
B. Y. Pekmezci ◽  
E. Y. Tuncel ◽  
A. Paksoy

Materials ◽  
2012 ◽  
Vol 5 (11) ◽  
pp. 2342-2352 ◽  
Author(s):  
Valentino Berardi ◽  
Geminiano Mancusi

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Duy-Thang Vu ◽  
Xuan-Linh Tran ◽  
Van-Duc Tran

This study investigates an adaptive-weighted instanced-based learning, for the prediction of the ultimate punching shear capacity (UPSC) of fiber-reinforced polymer- (FRP-) reinforced slabs. The concept of the new method is to employ the Differential Evolution to construct an adaptive instance-based regression model. The performance of the proposed model is compared to those of Artificial Neural Network (ANN) and traditional formula-based methods. A dataset which contains the testing results of FRP-reinforced concrete slabs has been collected to establish and verify new approach. This study shows that the investigated instance-based regression model is capable of delivering the prediction result which is far more accurate than traditional formulas and very competitive with the black-box approach of ANN. Furthermore, the proposed adaptive-weighted instanced-based learning provides a means for quantifying the relevancy of each factor used for the prediction of UPSC of FRP-reinforced slabs.


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