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Reverse design charts for flexural strength of steel-reinforced concrete beams based on artificial neural networks
Design of steel-reinforced concrete (SRC) beams can be a lengthy process, and understanding how design parameters as many as over 26 influence each other can be difficult. In this study, an artificial neural network was trained with large structural datasets to realize a reverse design of SRC beams based on mapping input parameters to output parameters rather than on structural mechanics or knowledge. For training, a back-substitution method was applied in two steps: reverse outputs are calculated in a reverse network of Step 1 for given reverse input parameters; then, reverse output parameters obtained in Step 1 are substituted as input parameters in forward network of Step 2 to obtain design parameters. Trained network was then implemented in determining design parameters of SRC beams for given unseen structural input parameters. Network results were ascertained through conventional structural calculations. Design charts offer predictions of multiple design parameters in any order, which can help engineers in the preliminary design of SRC beams. The developed network can be used to explore intricacies of design parameters to each other, which may be difficult with conventional design methods.
Reverse design charts for flexural strength of steel-reinforced concrete beams based on artificial neural networks
Design of steel-reinforced concrete (SRC) beams can be a lengthy process, and understanding how design parameters as many as over 26 influence each other can be difficult. In this study, an artificial neural network was trained with large structural datasets to realize a reverse design of SRC beams based on mapping input parameters to output parameters rather than on structural mechanics or knowledge. For training, a back-substitution method was applied in two steps: reverse outputs are calculated in a reverse network of Step 1 for given reverse input parameters; then, reverse output parameters obtained in Step 1 are substituted as input parameters in forward network of Step 2 to obtain design parameters. Trained network was then implemented in determining design parameters of SRC beams for given unseen structural input parameters. Network results were ascertained through conventional structural calculations. Design charts offer predictions of multiple design parameters in any order, which can help engineers in the preliminary design of SRC beams. The developed network can be used to explore intricacies of design parameters to each other, which may be difficult with conventional design methods.
Reverse design charts for flexural strength of steel-reinforced concrete beams based on artificial neural networks
Won-Kee Hong (Autor:in) / Dinh Han Nguyen (Autor:in) / Van Tien Nguyen (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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| Taylor & Francis Verlag | 2023
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