Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm

Assessment of bond behavior of glass fiber-reinforced polymer (GFRP) bars to concrete plays an important role in design and implementation of the polymer-matrix composites (PMCs). This study develops an optimized modeling strategy that harnesses the strong nonlinear mapping ability of artificial neu...

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Veröffentlicht in:	Composite structures 2017-02, Vol.161, p.441-452
Hauptverfasser:	Yan, Fei, Lin, Zhibin, Wang, Xingyu, Azarmi, Fardad, Sobolev, Konstantin
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural network (ANN) Artificial neural networks Bond behavior Bonding strength Compressive strength Concrete reinforcements Genetic algorithm (GA) Genetic algorithms Glass fiber reinforced plastics Learning theory Mathematical models Polymer-matrix composites (PMCs)
Online-Zugang:	Volltext
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