Shear strength prediction of HSC slender beams without web reinforcement
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作者:
H. M. Elsanadedy
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机构:King Saud University,MMB Chair for Research and Studies in Strengthening and Rehabilitation of Structures, Department of Civil Engineering, College of Engineering
H. M. Elsanadedy
H. Abbas
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机构:King Saud University,MMB Chair for Research and Studies in Strengthening and Rehabilitation of Structures, Department of Civil Engineering, College of Engineering
H. Abbas
Y. A. Al-Salloum
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机构:King Saud University,MMB Chair for Research and Studies in Strengthening and Rehabilitation of Structures, Department of Civil Engineering, College of Engineering
Y. A. Al-Salloum
T. H. Almusallam
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机构:King Saud University,MMB Chair for Research and Studies in Strengthening and Rehabilitation of Structures, Department of Civil Engineering, College of Engineering
T. H. Almusallam
机构:
[1] King Saud University,MMB Chair for Research and Studies in Strengthening and Rehabilitation of Structures, Department of Civil Engineering, College of Engineering
Shear strength;
High strength concrete;
Regression;
ANN;
Slender beams;
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摘要:
High-strength concrete (HSC) is currently popular in the design and construction of heavy structures. Being HSC a brittle material, its cracking is sudden and cracks traverse through the aggregate particles thus producing relatively smooth fracture planes which is contrary to the normal-strength concrete (NSC) wherein cracks go around the aggregate particles. The relatively smooth fracture planes in HSC considerably decrease the concrete shear strength by reducing the contribution of the aggregate interlock. However, the international codes for the design of reinforced concrete (RC) structures make no distinction between NSC and HSC and the assessment of shear capacity of slender RC beams without web steel is still based on the experimental data of beams of NSC. In the present research, an effort is made for predicting the shear strength of HSC slender RC beams without stirrups, using regression models and neural networks. A large database of experimental results for HSC slender beams, covering a wide range of influencing parameters, is used for regression analysis as well as for the development of the artificial neural network models. The results of analysis are compared with major code requirements as well as with the researchers’ design model. The regression based as well as the neural network based models are presented. New empirical design models for the two approaches are also suggested.
机构:
Belcan Engn Grp Inc, Caterpillar Champaign Simulat Ctr, Champaign, IL 61820 USAUniv Seoul, Sch Architecture & Architectural Engn, Seoul 130743, South Korea
Jung, Sungmoon
Kim, Kang Su
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机构:
Univ Seoul, Sch Architecture & Architectural Engn, Seoul 130743, South KoreaUniv Seoul, Sch Architecture & Architectural Engn, Seoul 130743, South Korea