Pressure-dependent bond stress-slip model for sand-coated FRP-concrete interface

2021 ◽  
Vol 263 ◽  
pp. 113719
Author(s):  
Hongwei Lin ◽  
Peng Feng ◽  
Jia-Qi Yang
Structures ◽  
2021 ◽  
Vol 34 ◽  
pp. 498-506
Author(s):  
Xiaoyong Lv ◽  
Zhiwu Yu ◽  
Zhi Shan

2014 ◽  
Vol 919-921 ◽  
pp. 1377-1380
Author(s):  
Xiao Dong Li ◽  
Hao Chen Feng ◽  
Wei Ning Yuan

This paper mainly gets a equation of crack width calculation. To get the result, the lineared bond-slip model between FRP bars and concrete is used, combining with the bond stress distribution model which is found by You Chunan. Finally, the value of this paper is compared with the value of ACI440.1R-06 and JSCE code, which prove the result is right.


2015 ◽  
Vol 112 (5) ◽  
Author(s):  
Canh N. Dang ◽  
Royce W. Floyd ◽  
Cameron D. Murray ◽  
W. Micah Hale ◽  
J. R. Martí-Vargas
Keyword(s):  

Materials ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3151 ◽  
Author(s):  
Xiaoyong Lv ◽  
Zhiwu Yu ◽  
Zhi Shan ◽  
Ju Yuan

The stochastic bond stress-slip behavior is an essential topic for the rebar-concrete interface. However, few theoretical models incorporating stochastic behavior in current literature can be traced. In this paper, a stochastic damage model based on micro-mechanical approach for bond stress-slip relationship of the interface under monotonic loading was proposed. In order to describe the mechanical behaviors of the rebar-concrete interface, a microscopic damage model was proposed. By introducing a micro-element consists of parallel spring element, friction element and a switch element, the model is formulated. In order to reflect the randomness of the bond stress-slip behavior contributed by the micro-fracture in the interface, a series of paralleled micro-elements are adopted with the failure threshold of individual spring element is set as a random variable. The expression of both mean and variance for the bond stress-slip relationship was derived based on statistical damage mechanics. Furthermore, by utilizing a search heuristic global optimization algorithm (i.e., a genetic algorithm), parameters of the proposed model are able to be identified from experimental results, which a lognormal distribution has adopted. The prediction was verified against experimental results, and it reveals that the proposed model is capable of capturing the random nature of the micro-structure and characterizing the stochastic behavior.


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