Effect of fine crack width and water cement ratio of SHCC on chloride ingress and rebar corrosion

2017 ◽  
Vol 80 ◽  
pp. 235-244 ◽  
Author(s):  
Koichi Kobayashi ◽  
Yuta Kojima
Keyword(s):  
Crack Width ◽  
Chloride Ingress ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Rebar Corrosion ◽  
Fine Crack

Study on the Mechanism of Rebar Corrosion Considering the Relationship between Corrosion and Crack Width

2007 ◽  
Vol 348-349 ◽  
pp. 481-484
Author(s):  
Jei Jun You ◽  
Han Seung Lee ◽  
Yoshiteru Ohno
Keyword(s):  
Stainless Steel ◽  
Crack Width ◽  
Corrosion Current ◽  
Reinforcement Corrosion ◽  
Accelerated Carbonation ◽  
Accelerated Corrosion ◽  
Cement Ratio ◽  
Accelerated Corrosion Tests ◽  
Rebar Corrosion ◽  
The Relationship

In this study, accelerated corrosion tests were conducted on concrete specimens with and without accelerated carbonation beforehand for the purpose of elucidating the effects of carbonation, cover depth, and water-cement ratio (W/C) on the reinforcement corrosion. During testing, the corrosion current between the anode steel and cathode stainless steel was measured to continuously monitor the progress of corrosion throughout the test period, thereby investigating the mechanism of reinforcement corrosion and the relationship between corrosion and crack width, as well as other parameters.

scholarly journals Influence of Crack Width on Chloride Penetration in Concrete Subjected to Alternating Wetting–Drying Cycles

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3801
Author(s):  
Jun Lai ◽  
Jian Cai ◽  
Qing-Jun Chen ◽  
An He ◽  
Mu-Yang Wei
Keyword(s):  
Crack Width ◽  
Chloride Concentration ◽  
Rc Beam ◽  
Rc Beams ◽  
Cross Sectional ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Steel Bar ◽  
Steel Bars ◽  
Average Loss

To investigate the durability of reinforced concrete (RC) beams under the combined actions of transverse cracks and corrosion, corrosion tests were conducted on a total of eight RC beams with different water–cement ratios and cracking states. The effects of the transverse crack width, water–cement ratio, and the length of the wetting–drying cycle on the distribution of the free chloride concentration, the cross-sectional loss of the tensile steel bars, and the chloride diffusion coefficient are analyzed. The results show that the widths of the transverse crack and the water–cement ratio of concrete greatly affected the chloride profile and content of the RC beam specimens. Specifically, the chloride contents in all the cracked RC beams at the depth of the steel bar exceeded the threshold value of 0.15%. As the width of the cracks increased, the chloride concentration and penetration of the cracked concrete beam increased. However, the chloride concentration at the reinforcement position did not seem to be obviously affected by increasing the wetting–drying cycles from 182 days to 364 days. Moreover, the decrease of the water–cement ratio effectively inhibited the penetration of chloride ions in the RC beam specimens. In terms of the cross-sectional loss of the steel bars, the average loss of the steel bar increases with increasing crack width for the beams with 182-day cycles, while the effect of crack width on the average loss is not as noticeable for the beams with 364-day cycles. Finally, a model is proposed to predict the relationship between the crack width influence coefficient, μ, and the crack width, w, and this model shows good agreement with the experimental results.

scholarly journals Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 44
Author(s):  
Fernando A. N. Silva ◽  
João M. P. Q. Delgado ◽  
Rosely S. Cavalcanti ◽  
António C. Azevedo ◽  
Ana S. Guimarães ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Cross Sections ◽  
Influential Factors ◽  
Average Error ◽  
Ann Model ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

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