Predicting Shear Capacity of RC Beams Strengthened with NSM FRP Using Neural Networks

Conference paper

Guler, O., Ahmed, H., Abbas, A. and Sharif, S. 2023. Predicting Shear Capacity of RC Beams Strengthened with NSM FRP Using Neural Networks. 3ICT 2023: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies. University of Bahrain, Bahrain 20 - 21 Nov 2023 IEEE.
AuthorsGuler, O., Ahmed, H., Abbas, A. and Sharif, S.
TypeConference paper
Abstract

This research aims to predict the shear capacity
of NSM FRP beams using the neural network method. The study investigates the key considerations and the necessary analysis for this prediction. NSM FRP beams are reinforced concrete beams that are strengthened with near-surface mounted (NSM) fiber-reinforced polymer (FRP) composites. Accurately predicting their shear capacity is important for ensuring their safety and reliability in real-world applications. The neural network method is a machine learning approach that is increasingly used in engineering analysis and design. The study explores how this method can be used to predict the shear capacity of NSM FRP beams and what factors should be taken into account in this analysis. The research also discusses the analytical approach required for this prediction, highlighting the necessary steps for obtaining accurate results. Overall, this study provides valuable insights into the use of the neural network method for predicting the shear capacity of NSM FRP beams. The findings can help inform future research and practical applications in the field of structural engineering, contributing to the development of safer and more reliable structures.

Year2023
Conference3ICT 2023: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies
PublisherIEEE
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Accepted14 Sep 2023
Deposited25 Sep 2023
Journal citationIn Press
Copyright holder© 2023, IEEE
Copyright informationPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study
Sharif, M., Abbod, Maysam, Sonoda, Luke I. and Sanghera, Bal 2013. Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study. British Journal of Applied Science & Technology. 4 (4), pp. 634-649. https://doi.org/10.9734/bjast/2014/5084
Preparation of 2D sequences of corneal images for 3D model building
Elbita, Abdulhakim, Qahwaji, Rami, Ipson, Stanley, Sharif, M. and Ghanchi, Faruque 2015. Preparation of 2D sequences of corneal images for 3D model building. Computer Methods and Programs in Biomedicine. 114 (2), pp. 194-205. https://doi.org/10.1016/j.cmpb.2014.01.009
Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images
Sharif, M., Qahwaji, R., Ipson, S. and Brahma, A. 2015. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Applied Soft Computing. 36 (Nov.), pp. 269-282. https://doi.org/10.1016/j.asoc.2015.07.019
An efficient intelligent analysis system for confocal corneal endothelium images
Sharif, M., Qahwaji, R., Shahamatnia, E., Alzubaidi, R., Ipson, S. and Brahma, A. 2015. An efficient intelligent analysis system for confocal corneal endothelium images. Computer Methods and Programs in Biomedicine. 122 (3), pp. 421-436. https://doi.org/10.1016/j.cmpb.2015.09.003
In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study
Alzubaidi, Rania, Sharif, M., Qahwaji, Rami, Ipson, Stanley and Brahma, Arun 2015. In Vivo Confocal Microscopic Corneal Images in health and disease with an emphasis on extracting features and visual signatures for corneal diseases: A review study. British Journal of Ophthalmology. 100 (1), pp. 41-55. https://doi.org/10.1136/bjophthalmol-2015-306934
A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing
Pizzamiglio, S., Naeem, U., ur Réhman, Shafiq, Sharif, M., Abdalla, H. and Turner, D. 2017. A Mutlimodal Approach to Measure the Levels Distraction of Pedestrians using Mobile Sensing. Procedia Computer Science. 113, pp. 89-96. https://doi.org/10.1016/j.procs.2017.08.297
A simplified finite element model for assessing steel fibre reinforced concrete structural performance
Abbas, A., Syed Mohsin, Sharifah M. and Cotsovos, Demetrios M. 2016. A simplified finite element model for assessing steel fibre reinforced concrete structural performance. Computers and Structures. 173, pp. 31-49. https://doi.org/10.1016/j.compstruc.2016.05.017
Shear behaviour of steel-fibre-reinforced concrete simply supported beams
Abbas, A., Syed Mohsin, Sharifah M., Cotsovos, Demetrios M. and Ruiz-Teran, Ana M. 2014. Shear behaviour of steel-fibre-reinforced concrete simply supported beams. Proceedings of the ICE - Structures and Buildings. 167 (9), pp. 544-558.
Statically-Indeterminate SFRC Columns under Cyclic Loads
Abbas, A., Mohsin, Sharifah, Cotsovos, Demetrios and Ruiz-Teran, Ana 2014. Statically-Indeterminate SFRC Columns under Cyclic Loads. Advances in Structural Engineering. 17 (10), pp. 1403-1418.
Seismic response of steel fibre reinforced concrete beam-column joints
Abbas, A., Syed Mohsin, Sharifah M. and Cotsovos, Demetrios M. 2014. Seismic response of steel fibre reinforced concrete beam-column joints. Engineering Structures. 59 (Feb14), pp. 261-283.