Journal
COMPOSITE STRUCTURES
Volume 161, Issue -, Pages 441-452Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compstruct.2016.11.068
Keywords
Bond behavior; Polymer-matrix composites (PMCs); Artificial neural network (ANN); Genetic algorithm (GA)
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Funding
- ND NASA EPSCoR
- ND NSF EPSCoR
- Hughes Brother Inc.
- Office of Integrative Activities
- Office Of The Director [1355466] Funding Source: National Science Foundation
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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 neural network (ANN) with the global searching ability of genetic algorithm (GA) for bond strength prediction. The factors that affect the bond strength were identified from the test data of 157 beam-test specimens in the literature, in terms of bar conditions (bar diameter, surface, position and embedment length), concrete (thickness of concrete cover and concrete compressive strength), and confinement from transverse reinforcements. Comparison of the bond strengths predicted by the proposed optimized ANN-GA model with test results showed a higher accuracy with less scatter compared to the conventional ANN model. (C) 2016 Elsevier Ltd. All rights reserved.
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