scholarly journals SHEAR BEHAVIOR IN PRECRACKED CONCRETE UNDER CYCLIC LOADING AT CONSTANT CRACK WIDTH

Author(s):  
Yasuji SHINOHARA ◽  
Katsumasa KAWAMICHI ◽  
Sachiko ISHITOBI
Author(s):  
Shi-Jin Feng ◽  
Jia-Liang Shi ◽  
Yang Shen ◽  
Hong-Xin Chen ◽  
Ji-Yun Chang

Materials ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 1682 ◽  
Author(s):  
Jun Zhao ◽  
Jingchao Liang ◽  
Liusheng Chu ◽  
Fuqiang Shen

Many researchers have performed experimental and theoretical studies on the shear behavior of steel fiber reinforced concrete (SFRC) beams with conventional reinforcement; few studies involve the shear behavior of SFRC beams with high-strength reinforcement. In this paper, the shear test of eleven beams with high-strength reinforcement was carried out, including eight SFRC beams and three reinforced concrete (RC) beams. The load-deflection curve, concrete strain, stirrup strain, diagonal crack width, failure mode and shear bearing capacity of the beams were investigated. The test results show that steel fiber increases the stiffness, ultimate load and failure deformation of the beams, but the increase effect of steel fiber decreases with the increase of stirrup ratio. After the diagonal crack appears, steel fiber reduces the concrete strains of the diagonal section, stirrup strains and diagonal crack width. In addition, steel fiber reduces crack height and increases crack number. Finally, the experimental values of the shear capacities were compared with the values calculated by CECS38:2004 and ACI544.4R, and the equation of shear capacity in CECS38:2004 was modified to effectively predict the shear capacities of SFRC beams with high-strength reinforcement.


2017 ◽  
Vol 143 ◽  
pp. 398-409 ◽  
Author(s):  
Hyun-Do Yun ◽  
Sun-Woo Kim ◽  
Wan-Shin Park ◽  
Young-Il Jang

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Sen Pang ◽  
Bo Diao ◽  
Yinghua Ye ◽  
Shuxin Chen ◽  
Xin Wang

An experimental study was conducted to investigate the impact of cyclic loading on the mechanical performance and chloride diffusivity of RC beams exposed to seawater wet-dry cycles. To induce initial damage to RC beam specimen, cyclic loading controlled by max load and cycles was applied. Then beam specimens underwent 240 wet-dry cycles of seawater. Results show that the chloride content increased as max load and cycle increased. The chloride content at steel surface increased approximatively linearly as average crack width increased. Moreover, the max load had more influence on chloride content at steel surface than cycle. The difference of average chloride diffusion coefficient between tension and compression concrete was little at uncracked position. Average chloride diffusion coefficient increased as crack width increased when crack width was less than 0.11 mm whereas the increasing tendency was weak when crack width exceeded 0.11 mm. The residual yield load and ultimate load of RC beams decreased as max load and cycle increased. Based on univariate analysis of variance, the max load had more adverse effect on yield load and ultimate load than cycle.


2021 ◽  
Vol 11 (16) ◽  
pp. 7700
Author(s):  
Reventheran Ganasan ◽  
Chee Ghuan Tan ◽  
Zainah Ibrahim ◽  
Fadzli Mohamed Nazri ◽  
Muhammad M. Sherif ◽  
...  

In recent years, researchers have investigated the development of artificial neural networks (ANN) and finite element models (FEM) for predicting crack propagation in reinforced concrete (RC) members. However, most of the developed prediction models have been limited to focus on individual isolated RC members without considering the interaction of members in a structure subjected to hazard loads, due to earthquake and wind. This research develops models to predict the evolution of the cracks in the RC beam-column joint (BCJ) region. The RC beam-column joint is subjected to lateral cyclic loading. Four machine learning models are developed using Rapidminer to predict the crack width experienced by seven RC beam-column joints. The design parameters associated with RC beam-column joints and lateral cyclic loadings in terms of drift ratio are used as inputs. Several prediction models are developed, and the highest performing neural networks are selected, refined, and optimized using the various split data ratios, number of inputs, and performance indices. The error in predicting the experimental crack width is used as a performance index.


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