Please use this identifier to cite or link to this item: http://repository.aaup.edu/jspui/handle/123456789/1896
Title: ML-CCD: machine learning model to predict concrete cover delamination failure mode in reinforced concrete beams strengthened with FRP sheets
Authors: Salahat, Fahed$Other$Palestinian
Rasheed, Hayder$Other$Other
Ashqar, Huthaifa$AAUP$Palestinian
Issue Date: 20-Jul-2024
Publisher: Elsevier
Citation: Salahat, F. H., Rasheed, H. A., & Ashqar, H. I. (2024). ML-CCD: machine learning model to predict concrete cover delamination failure mode in reinforced concrete beams strengthened with FRP sheets. Software Impacts, 100685.
Abstract: ML-CCD is an open-source Python software based on a Machine-Learning model that was utilized to predict the premature failure of reinforced concrete (RC) beams strengthened with Fiber Reinforced Polymers (FRP). The model was trained using a database consisting of 70 experimentally tested beams that failed prematurely due to Concrete Cover Delamination (CCD). The significant beams parameters that influence the CCD failure were used in training the ML-CCD. This software predicts the ultimate strain in the FRP sheets at failure, thus finding its ultimate tensile strength and the effective strengthening ratio for design purposes.
URI: http://repository.aaup.edu/jspui/handle/123456789/1896
ISSN: https://doi.org/10.1016/j.simpa.2024.100685
Appears in Collections:Faculty & Staff Scientific Research publications

Files in This Item:
File Description SizeFormat 
MLCCD.pdf3.18 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Admin Tools