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المؤلفAborahmeh, Qusai
المؤلفRabie, Mohamed
المؤلفEbead, Usama
تاريخ الإتاحة2023-08-27T05:58:09Z
تاريخ النشر2023
اسم المنشور2nd International Conference on Civil Infrastructure and Construction
الاقتباسAborahmeh Q., Rabie M. & Ebead U., "Parametric Optimization of RC Beams Strengthened with FRCM Using Finite Element Modelling and Response Surface Methodology", The 2nd International Conference on Civil Infrastructure and Construction (CIC 2023), Doha, Qatar, 5-8 February 2023, DOI: https://doi.org/10.29117/cic.2023.0074
الرقم المعياري الدولي للكتاب2958-3128
معرّف المصادر الموحدhttps://doi.org/10.29117/cic.2023.0074
معرّف المصادر الموحدhttp://hdl.handle.net/10576/46777
الملخصThis study focuses on the numerical and statistical analyses to predict the mid-span moment capacity of RC beams strengthened with fabric reinforcement cementitious mortar (FRCM) laminate. A finite element model (FEM) has been built to simulate twelve RC beams strengthened with two types of FRCM, namely Polyparaphenylene benzobisoxazole (PBO) FRCM and Carbon (C) FRCM. The FE models were verified based on experimental work available in the literature. The finite element models have shown a good agreement with experimental results in terms of maximum load-carrying capacity, load-deflection curves, and concrete strain values. The numerical simulation was followed by a parametric study on 42 models using face centred response surface methodology (RSM). Combining FEM and RSM, a novel mathematical model has been proposed to predict the mid-span moment capacity of the RC beams strengthened with FRCM. The results of the proposed model have shown optimal predictability with R2 equal to 90.34%. In addition, the proposed model agreed with the ACI design procedures and the existing literature.
اللغةen
الناشرQatar University Press
الموضوعFEM
Nonlinear modelling
FRCM
Strengthening
Response surface method
العنوانParametric Optimization of RC Beams Strengthened with FRCM Using FE Modelling and Response Surface Methodology
النوعConference Paper
الصفحات555-562
ESSN2958-3136


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