Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Reliable Machine Learning Model for Shear Strength of Reinforced Concrete Beams Strengthened Using FRP Jackets

Version 1 : Received: 11 May 2022 / Approved: 12 May 2022 / Online: 12 May 2022 (10:44:06 CEST)
Version 2 : Received: 22 August 2022 / Approved: 23 August 2022 / Online: 23 August 2022 (03:27:32 CEST)

How to cite: Gasser, M.; Mahmoud, O.; elsayed, T.; Deifalla, A. Reliable Machine Learning Model for Shear Strength of Reinforced Concrete Beams Strengthened Using FRP Jackets. Preprints 2022, 2022050170. https://doi.org/10.20944/preprints202205.0170.v2 Gasser, M.; Mahmoud, O.; elsayed, T.; Deifalla, A. Reliable Machine Learning Model for Shear Strength of Reinforced Concrete Beams Strengthened Using FRP Jackets. Preprints 2022, 2022050170. https://doi.org/10.20944/preprints202205.0170.v2

Abstract

All over the world, externally bonded fiber-reinforced polymer systems used to strengthen concrete elements improve building sustainability. However, reports issued by the American Concrete Institute Committee 440 called for heavy scrutinizing before actual field implementation. The very limited number of proposed equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine learning techniques are being implemented successfully in developing strength models for complex problems. This study aims to provide a reliable machine learning model based on an experimental database. The proposed model was developed and validated against the experimental database and the very limited models in the literature. The model showed improved agreement with the experimental results compared to the previous models.

Keywords

Shear Strength; FRP; Anchorage devices; effective FRP strain

Subject

Engineering, Civil Engineering

Comments (1)

Comment 1
Received: 23 August 2022
Commenter: Ahmed Deifalla
Commenter's Conflict of Interests: Author
Comment: Revised version
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