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2023 | OriginalPaper | Chapter

12. Punching Shear Strength of FRP-Reinforced-Concrete Using a Machine Learning Model

Authors : Nermin M. Salem, Ahmed F. Deifalla

Published in: Advances in Smart Materials and Innovative Buildings Construction Systems

Publisher: Springer Nature Switzerland

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Abstract

The aim of this research is to investigate the strength behavior of Fiber reinforced polymers (FRP) - Reinforced-concrete using supervised Machine Learning (ML) techniques. Based on previous studies by the authors, two machine learning modes were found to be the most effective in terms of accuracy and consistency, namely, the ensembled boosted regression model and the medium Gaussian SVM. The ensembled boosted model showed the most accurate predictions. To assess the performance of the two suggested ML models: the 15-held-out validation method and statistical analysis techniques including metrics such as the coefficient of variation (\({R}^{2}\)), mean absolute error (MAE), and root mean square error (RMSE) are used. The ensembled boosted ML model demonstrated the most accurate predictions, achieving \({R}^{2}\) = 0.97, MAE = 43.352, and least RMSE = 71.963. Also, the variation of strength versus effective parameters was captured and discussed.

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Literature
go back to reference Ali A, Hamady M, Chalioris CE, Deifalla A (2021) Evaluation of the shear design equations of FRP-reinforced concrete beams without shear reinforcement. Eng Struct 235. Elsevier Ali A, Hamady M, Chalioris CE, Deifalla A (2021) Evaluation of the shear design equations of FRP-reinforced concrete beams without shear reinforcement. Eng Struct 235. Elsevier
go back to reference Deifalla A (2021a) Refining the torsion design of fibered concrete beams reinforced with FRP using multi-variable non-linear regression analysis for experimental results. Eng Struct 224. Elsevier Deifalla A (2021a) Refining the torsion design of fibered concrete beams reinforced with FRP using multi-variable non-linear regression analysis for experimental results. Eng Struct 224. Elsevier
go back to reference Deifalla A, Salem NM (2022) A machine learning model for torsion strength of externally bonded FRP-reinforced concrete beams. Polymers 14(9):1824 Deifalla A, Salem NM (2022) A machine learning model for torsion strength of externally bonded FRP-reinforced concrete beams. Polymers 14(9):1824
go back to reference Salem NM, Mahdi HMK, Abbas H (2018) Semantic image inpainting using self-learning encoder-decoder and adversarial loss. In: Proceeding of 13th international conference on computer engineering and systems (ICCES) Salem NM, Mahdi HMK, Abbas H (2018) Semantic image inpainting using self-learning encoder-decoder and adversarial loss. In: Proceeding of 13th international conference on computer engineering and systems (ICCES)
go back to reference Salem NM, Mahdi HMK, Abbas HM (2019) Random-shaped image inpainting using dilated convolution. Int J Eng Adv Technol (IJEAT) 8(6) Salem NM, Mahdi HMK, Abbas HM (2019) Random-shaped image inpainting using dilated convolution. Int J Eng Adv Technol (IJEAT) 8(6)
go back to reference Salem NM, Mahdi HMK, Abbas HM (2020) A novel face inpainting approach based on guided deep learning. In 4th international conference on communications, signal processing and their applications (ICCSPA ‘20) Salem NM, Mahdi HMK, Abbas HM (2020) A novel face inpainting approach based on guided deep learning. In 4th international conference on communications, signal processing and their applications (ICCSPA ‘20)
Metadata
Title
Punching Shear Strength of FRP-Reinforced-Concrete Using a Machine Learning Model
Authors
Nermin M. Salem
Ahmed F. Deifalla
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-47428-6_12