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- 저자 : Heniegal, (검색결과 2 건)
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Ibrahim, Omar Mohamed Omar,Heniegal, Ashraf Mohamed,Ibrahim, Khamis Gamal,Agwa, Ibrahim Saad Techno-Press 2020 Advances in concrete construction Vol.10 No.5
This study investigated the effect of horizontal casting joints on the mechanical properties and structural behavior of sustainable self-compacting reinforced concrete beams (SCRCB). The experimental research consisted of two stages. The first stage used four types of concrete mixtures which were produced to indicate the effects of cement replaced with cement waste at 0%, 5%, 10%, and 15% by weight of cement content on fresh concrete properties of self-compacting concrete (SCC) such as, passing ability, filling ability, and segregation resistance. In addition, mechanical properties such as compressive, tensile, and flexural strength were also studied. The second stage selected the best mixture from the first stage and studied the effect of horizontal casting joints on the structural behavior of sustainable SCRCBs. The effect of horizontal casting joints on the mechanical properties and structural behavior were at the 25%, 50%, 75%, and 100% of sample height. Load deflection, failure mode, and theoretical analysis were studied. Results indicated that the incorporation of replacement with cement waste by 5% to 10% led to economic and environmental advantages, and the results were acceptable for fresh and mechanical properties. The results indicated that delaying the time for casting the second layer and increasing the cement waste in concrete mixtures had a great effect on the mechanical properties of SCC. The ultimate load capacity of horizontal casting joints reinforced concrete beams slightly decreased compared with the control beam. The maximum deflection of casting joint beams with 75% of samples height is similar with the control beam. The experimental results of reinforced concrete beams were substantially acceptable with the theoretical results. The failure modes obtained the best forced casting joint on the structural behavior at 50% height of casting in the beam.
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Ahmed M. Tahwia,Ashraf Heniegal,Mohamed S. Elgamal,Bassam A. Tayeh 사단법인 한국계산역학회 2021 Computers and Concrete, An International Journal Vol.27 No.1
The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model’s statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.
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