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A nouvelle approach for predicting the shear cracking angle in RC and PC beams using artificial neural networks

  • The truss model for predicting shear resistance of reinforced concrete beams has usually been criticized because of its underestimation of the concrete shear strength especially for beams with low shear reinforcement. Two challengers are commonly encountered in any truss model and are responsible for its inaccurate shear strength prediction. First: the cracking angle is usually assumed empirically and second the shear contribution of the arching action is usually neglected. This research introduces a nouvelle approach, by using Artificial Neural Network (ANN) for accurately evaluating the shear cracking angle of reinforced and prestressed concrete beams. The model inputs include the beam geometry, concrete strength, the shear reinforcement ratio and the prestressing stress if any. ...

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Document Type:Conference Proceeding
Author: M. M. Reda Taha, Alaa Sherif, Josef Hegger
DOI (Cite-Link):https://doi.org/10.25643/bauhaus-universitaet.107Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20111215-1071Cite-Link
Date of Publication (online):2004/11/01
Year of first Publication:2004
Release Date:2004/11/01
Institutes:Fakultät Bauingenieurwesen / Professur Informatik im Bauwesen
GND Keyword:Neuronales Netz; Wasserbau; Scherung; Rissbildung
Dewey Decimal Classification:600 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
BKL-Classification:54 Informatik / 54.89 Angewandte Informatik: Sonstiges
56 Bauwesen / 56.03 Methoden im Bauingenieurwesen
Collections:Bauhaus-Universität Weimar / International Conference on Computing in Civil and Building Engineering, ICCCBE, Weimar / International Conference on Computing in Civil and Building Engineering, ICCCBE, Weimar 10. 2004
Licence (German):License Logo In Copyright