Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Predicting NSMR–concrete bond strength using artificial neural networks: A comparative‐analysis study
Near Surface‐Mounted Fiber Reinforced Polymer (NSM FRP) strips have become a popular technique for the rehabilitation of existing deteriorated concrete structures, the effectiveness of which is vitally related to its bond to concrete. The present Artificial Neural Network (ANN) model, aimed at providing precise predictions for bond strength, utilizes nearly 550 refined dataset points and accounts for more influential parameters, namely elastic modulus, cross‐sectional area, bond length of FRP strips, groove size, concrete edge distance, groove spacing, and concrete's compressive strength. The very few relevant publications for the prediction of bond strength between NSM FRP composites and concrete employing artificial intelligence bond models considered limited database sizes and few key parameters. The Matlab software was utilized to build, train, and test the network model using seven input variables and one targeted output. Detailed statistical analysis was presented to test the model's validity with Taylor charts produced using the Matlab software for the purpose of comparing the performance of the present model against different literature models. The proposed ANN model showed a high prediction ability and a low mean square error. Indeed, the mean square error for the testing and validation data remained below 0.00055, whereas the coefficient of determination exceeded 0.99. The error charts for training and testing database revealed normal distribution of bond strength residuals; supporting further the validity of the proposed model. The trending behavior of the ultimate bond force versus the key parameters was consistent with that captured in the various literature models. The statistical and the sensitively analyses performed stipulated the validity of the present model for producing reliable prediction for bond strength between NSM FRP strips and concrete.
Predicting NSMR–concrete bond strength using artificial neural networks: A comparative‐analysis study
Near Surface‐Mounted Fiber Reinforced Polymer (NSM FRP) strips have become a popular technique for the rehabilitation of existing deteriorated concrete structures, the effectiveness of which is vitally related to its bond to concrete. The present Artificial Neural Network (ANN) model, aimed at providing precise predictions for bond strength, utilizes nearly 550 refined dataset points and accounts for more influential parameters, namely elastic modulus, cross‐sectional area, bond length of FRP strips, groove size, concrete edge distance, groove spacing, and concrete's compressive strength. The very few relevant publications for the prediction of bond strength between NSM FRP composites and concrete employing artificial intelligence bond models considered limited database sizes and few key parameters. The Matlab software was utilized to build, train, and test the network model using seven input variables and one targeted output. Detailed statistical analysis was presented to test the model's validity with Taylor charts produced using the Matlab software for the purpose of comparing the performance of the present model against different literature models. The proposed ANN model showed a high prediction ability and a low mean square error. Indeed, the mean square error for the testing and validation data remained below 0.00055, whereas the coefficient of determination exceeded 0.99. The error charts for training and testing database revealed normal distribution of bond strength residuals; supporting further the validity of the proposed model. The trending behavior of the ultimate bond force versus the key parameters was consistent with that captured in the various literature models. The statistical and the sensitively analyses performed stipulated the validity of the present model for producing reliable prediction for bond strength between NSM FRP strips and concrete.
Predicting NSMR–concrete bond strength using artificial neural networks: A comparative‐analysis study
Haddad, Rami (Autor:in) / Qarqaz, Noor (Autor:in)
Structural Concrete ; 24 ; 6421-6435
01.10.2023
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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