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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2019

09.08.2015 | Theory and Applications of Soft Computing Methods

Development of prediction models for shear strength of SFRCB using a machine learning approach

verfasst von: Masoud Sarveghadi, Amir H. Gandomi, Hamed Bolandi, Amir H. Alavi

Erschienen in: Neural Computing and Applications | Ausgabe 7/2019

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Abstract

In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.
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Metadaten
Titel
Development of prediction models for shear strength of SFRCB using a machine learning approach
verfasst von
Masoud Sarveghadi
Amir H. Gandomi
Hamed Bolandi
Amir H. Alavi
Publikationsdatum
09.08.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1997-6

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