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Results in Journal Computers & concrete: 767

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Redha Yeghnem, Hicham Zakaria Guerroudj, Lemya Hanifi Hachemi Amar, Sid Ahmed Meftah, , , El Abbas Adda Bedia
Published: 25 May 2017
Computers & concrete, Volume 19, pp 579-588; https://doi.org/10.12989/cac.2017.19.5.579

, Habib Uysal
Published: 25 May 2017
Computers and Concrete, Volume 19, pp 589-597; https://doi.org/10.12989/cac.2017.19.5.589

Abstract:
Reinforcement detailing of a corbel via an integrated strut-and-tie modeling approach computer-aided design;strut-and-tie model;structural optimization;performance decision;reinforced concrete;steel reinforcement; Strut-and-tie modeling method, which evolved on truss-model approach, has generally been preferred for the design of complex reinforced concrete structures and structural elements that have critical shear behavior. Some structural members having disturbed regions require exceptional detailing for all support and loading conditions, such as the beam-column connections, deep beams, short columns or corbels. Considering the general expectation of exhibiting brittle behavior, corbels are somewhat dissimilar to other shear critical structures. In this study, reinforcement layout of a corbel model was determined by the participation of structural optimization and strut-and-tie modeling methods, and an experimental comparison was performed against a conventionally designed model.
Alper Bideci, Ozlem Salli Bideci, Sabit Oymael, Ali Haydar Gultekin, Hasan Yildirim
Published: 25 May 2017
Computers & concrete, Volume 19, pp 451-455; https://doi.org/10.12989/cac.2017.19.5.451

How-Ji Chen, Pang-Hsu Tai, Ching-Fang Peng,
Published: 25 April 2017
Computers & concrete, Volume 19, pp 413-417; https://doi.org/10.12989/cac.2017.19.4.413

, Hamid Reza Nejati, Abdolhadi Ghazvinian, Alireza Najigivi
Published: 25 April 2017
Computers & concrete, Volume 19, pp 429-434; https://doi.org/10.12989/cac.2017.19.4.429

Ebrahim Khalilzadeh Vahidi, Maryam Mokhtari Malekabadi, , Mohammad Mahdi Roshani, Gholam Hossein Roshani
Published: 25 April 2017
Computers & concrete, Volume 19, pp 435-442; https://doi.org/10.12989/cac.2017.19.4.435

Qi Pu, Yan Yao, Ling Wang, Xingxiang Shi, Jingjing Luo, Yifei Xie
Published: 25 March 2017
Computers & concrete, Volume 19, pp 257-262; https://doi.org/10.12989/cac.2017.19.3.257

Abstract:
The investigation of pH threshold value on the corrosion of steel reinforcement in concrete carbonation;corrosion;steel reinforcement;pH threshold; The aim of this study is to investigate the pH threshold value for the corrosion of steel reinforcement in concrete. A method was designed to attain the pH value of the pore solution on the location of the steel in concrete. Then the pH values of the pore solution on the location of steel in concrete were changed by exposing the samples to the environment (CO25%, RH 40%) to accelerate carbonation with different periods. Based on this, the pH threshold value for the corrosion of steel reinforcement had been examined by the methods of half-cell potential and electrochemical impedance spectra (EIS). The results have indicated that the pH threshold value for the initial corrosion of steel reinforcement in concrete was 11.21. However, in the carbonated concrete, agreement among whether steel corrosion was initiatory determined by the detection methods mentioned above could be found.
Jiongfeng Liang, Yu Deng, Minghua Hu, Dilian Tang
Published: 25 March 2017
Computers & concrete, Volume 19, pp 227-232; https://doi.org/10.12989/cac.2017.19.3.227

Guang-Ji Yin, , Yu-Juan Tang, Olawale Ayinde, Dong-Nan Ding
Published: 25 February 2017
Computers & concrete, Volume 19, pp 143-152; https://doi.org/10.12989/cac.2017.19.2.143

, Farzan Mahboubi
Published: 25 February 2017
Computers & concrete, Volume 19, pp 203-210; https://doi.org/10.12989/cac.2017.19.2.203

Abstract:
Evaluating the settlement of lightweight coarse aggregate in self-compacting lightweight concrete self-compacting;concrete;settlement;segregation;image processing;fuzzy logic; The purpose of this paper is to evaluate the settlement of lightweight coarse aggregate of self-compacting lightweight concrete (SCLC) after placement of concrete on its final position. To investigate this issue, sixteen samples of concrete mixes were made. The water to cementitious materials ratios of the mixes were 0.35 and 0.4. In addition to the workability tests of self-compacting concrete (SCC) such as slump flow, V-funnel and L-box tests, a laboratory experiment was made to examine the segregation of lightweight coarse aggregate in concrete. Because of the difficulties of this test, the image processing technique of MATLAB software was used to check the segregation above too. Moreover, the fuzzy logic technique of MATLAB software was utilized to improve the clarity of the borders between the coarse aggregate and the paste of the mixtures. At the end, the results of segregation tests and software analyses are given and the accuracy of the software analyses is evaluated. It is worth noting that the minimum and maximum differences between the results of laboratory tests and software analyses were 1.2% and 9.19% respectively. It means, the results of image processing technique looks exact enough for estimating the segregation of lightweight coarse aggregate in SCLC.
Published: 25 February 2017
Computers & concrete, Volume 19, pp 217-226; https://doi.org/10.12989/cac.2017.19.2.217

Abstract:
Prediction of compressive strength of lightweight mortar exposed to sulfate attack cement mortar;silica fume;fly ash;compressive strength;modeling;sulfates/sulfate resistance; This paper summarizes the results of experimental research, and artificial intelligence methods focused on determination of compressive strength of lightweight cement mortar with silica fume and fly ash after sulfate attack. The artificial neural network and the support vector machine were selected as artificial intelligence methods. Lightweight cement mortar mixtures containing silica fume and fly ash were prepared in this study. After specimens were cured in $20{\pm}2^{\circ}C$ waters for 28 days, the specimens were cured in different sulfate concentrations (0%, 1% $MgSO_4^{-2}$, 2% $MgSO_4^{-2}$, and 4% $MgSO_4^{-2}$ for 28, 60, 90, 120, 150, 180, 210 and 365 days. At the end of these curing periods, the compressive strengths of lightweight cement mortars were tested. The input variables for the artificial neural network and the support vector machine were selected as the amount of cement, the amount of fly ash, the amount of silica fumes, the amount of aggregates, the sulfate percentage, and the curing time. The compressive strength of the lightweight cement mortar was the output variable. The model results were compared with the experimental results. The best prediction results were obtained from the artificial neural network model with the Powell-Beale conjugate gradient backpropagation training algorithm.
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