Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting fiber‐reinforced polymer–concrete bond strength using artificial neural networks: A comparative analysis study
The repair efficiency of fiber‐reinforced polymer (FRP) is crucially linked to bond strength between FRP and concrete. Artificial neural networks (ANNs) technique is employed for the prediction of FRP–concrete bond strength based on more than 440 data points collected from literature work for training and testing of the proposed ANNs model. Such a model facilitates investigating the effect of various key parameters in controlling the bond. These are concrete compressive strength, maximum aggregate size, FRP thickness and modulus of elasticity, FRP‐to‐concrete length and width ratios, and adhesive tensile strength. The proposed ANNs model shows high fitting and prediction capability of training and testing data, respectively, with low mean square errors. Its accuracy of prediction far exceeds that of literature empirical models. Furthermore, the present comparative and sensitivity study of the predicted bond strength promotes the understanding of the impact of the above key parameters.
Predicting fiber‐reinforced polymer–concrete bond strength using artificial neural networks: A comparative analysis study
The repair efficiency of fiber‐reinforced polymer (FRP) is crucially linked to bond strength between FRP and concrete. Artificial neural networks (ANNs) technique is employed for the prediction of FRP–concrete bond strength based on more than 440 data points collected from literature work for training and testing of the proposed ANNs model. Such a model facilitates investigating the effect of various key parameters in controlling the bond. These are concrete compressive strength, maximum aggregate size, FRP thickness and modulus of elasticity, FRP‐to‐concrete length and width ratios, and adhesive tensile strength. The proposed ANNs model shows high fitting and prediction capability of training and testing data, respectively, with low mean square errors. Its accuracy of prediction far exceeds that of literature empirical models. Furthermore, the present comparative and sensitivity study of the predicted bond strength promotes the understanding of the impact of the above key parameters.
Predicting fiber‐reinforced polymer–concrete bond strength using artificial neural networks: A comparative analysis study
Haddad, Rami (Autor:in) / Haddad, Madeleine (Autor:in)
Structural Concrete ; 22 ; 38-49
01.02.2021
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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