Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes

Advances in Engineering Software

Volumn 41, Issue 7-8, 2010, Pages 946-955

  Arslan, M Hakan ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Artificial neural network; Back propagation algorithm; Building code; Reinforced concrete beam; Theoretical model; Torsional strength

Indexed keywords

BACKPROPAGATION ALGORITHMS; BUILDING CODES; COMPOSITE BEAMS AND GIRDERS; COMPRESSIVE STRENGTH; CONCRETE BEAMS AND GIRDERS; CONCRETE BUILDINGS; CRACK PROPAGATION; DATA FLOW ANALYSIS; FORECASTING; REINFORCED CONCRETE; REINFORCEMENT; TORSIONAL STRESS;

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK ALGORITHM; ARTIFICIAL NEURAL NETWORKS; CODE EQUATIONS; CONCRETE COMPRESSIVE STRENGTH; CROSS SECTIONAL AREA; EXISTING BUILDING; EXPERIMENTAL DATA; INPUT PARAMETER; INPUT VECTOR; LONGITUDINAL REINFORCEMENT; OUTPUT VECTORS; PERFORMANCE EVALUATION; PURE TORSION; RC BEAMS; REINFORCED CONCRETE BEAMS; STEEL RATIO; TEST DATA; THEORETICAL MODELS; TORSIONAL STRENGTH; ULTIMATE TORSIONAL STRENGTH; YIELD STRENGTH;

NEURAL NETWORKS;

EID: 77955472158     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2010.05.009     Document Type: Article

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