Abstract
In this study, an Artificial Neural Network (ANN) model to predict the moment capacity of Fiber Reinforced Plastic (FRP) strengthened Reinforced Concrete (RC) beams exposed to fire is developed. The software ABAQUS heat transfer analysis is verified by comparison with the fire resistance test results. Through this heat transfer analysis, the temperature distribution of the beam section is determined, and 400 datasets are obtained using the moment capacity calculation method combined with the section equilibrium method. The data consist of eight input parameters: the beam width, beam height, FRP area, rebar area, concrete compressive strength, insulation thickness, concrete cover depth, and fire exposure time. The output parameter is the moment capacity. The ANN model is developed through a sensitivity study using the algorithm type and the number of hidden-layer neurons as variables. The average error between the predicted data of the developed ANN model and the target data obtained from the moment capacity calculation method was 0.35 kN·m, and the average relative error was 0.2512%, showing high accuracy. Therefore, the ANN model developed here can determine the moment capacity without complex calculations. The effects of the input parameters on the moment capacity of the FRP-strengthened RC beams exposed to fire are investigated using the ANN model.
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References
ABAQUS (2010) Abaqus 6.10: analysis user’s manual. Dassault Systemes Simulia Corp: Providence, RI
ACI 440.2R-08 (2008) ACI 440.2R-08: Guide for the design and construction of externally bonded FRP systems for strengthening concrete structures. American Concrete Institute, Farmington Hills
ASTM E 119-11 (2010) Standard methods of fre tests of building construction and materials, West Conshohocken, PA, USA
Ahmed A, Kodur VKR (2011a) Effect of bond degradation on fire resistance of FRP-strengthened reinforced concrete beams. Composites Part B: Engineering 42.2:226–237, DOI: https://doi.org/10.1016/j.compositesb.2010.11.004
Ahmed A, Kodur VKR (2011b) The experimental behavior of FRP-strengthened RC beams subjected to design fire exposure. Engineering Structures 33(7):2201–2211, DOI: https://doi.org/10.1016/j.engstruct.2011.03.010
Bisby LA (2003) Fire behaviour of fibre-reinforced polymer (FRP) reinforced or confined concrete. PhD Thesis, Queen’s University at Kingston, Canada
Bengar HA, Abdollahtabar M, Shayanfar J (2016) Predicting the ductility of RC beams using nonlinear regression and ANN. Iranian Journal of Science and Technology, Transactions of Civil Engineering 40(4):297–310, DOI: https://doi.org/10.1007/s40996-016-0033-0
Bakis CE, Bank LC, Brown V, Cosenza E, Davalos J, Lesko J, Machida A, Rizkalla S, Triantafillou T (2002) Fiber-reinforced polymer composites for construction—state-of-the-art review. Journal of composites for construction 6(2):73–87, DOI: https://doi.org/10.1061/(ASCE)1090-0268(2002)6:2(73)
Bergman T, Incropera F, Lavine A, DeWitt D (2011) Introduction to heat transfer. Wiley, Hoboken
Borchert K, Zilch K (2005) Time depending thermo mechanical bond behavior of epoxy bonded pre-stressed FRP-reinforcement. Special Publication 230:671–684
Camata G, Pasquini F, Spacone E (2007) High Temperature Flexural Strengthening with Externally Bonded FRP Reinforcement. In: Proceedings of 8th International Symposium on Fiber Reinforced Polymer (FRP) Reinforcement for Concrete Structures (FRP8RCS):1–10
Cai B, Xu L-F, Fu F (2019) Shear resistance prediction of postfre reinforced concrete beams using artifcial neural network. International Journal of Concrete Structures and Materials 13(1):46, DOI: https://doi.org/10.1186/s40069-019-0358-8
Eurocode 2 (2004) Design of concrete structures - Part 1-1: General rules and rules for buildings. European Committee for Standardization (CEN) London, UK
Eurocode 4 (2005) Design of composite steel and concrete structures - Part 1-2: General Rules-Structural Fire Design. European Committee for Standardization (CEN) London, UK
Erdem H (2010) Prediction of moment capacity of reinforced concrete slabs in fire using artificial neural networks. Advances in Engineering Software 41(2):270–276, DOI: https://doi.org/10.1016/j.advengsoft.2009.07.006
Erdem H (2015) Predicting the moment capacity of RC beams exposed to fire using ANNs. Construction and Building Materials 101(Part 1):30–38 DOI: https://doi.org/10.1016/j.conbuildmat.2015.10.049
Haido JH (2022) Prediction of the shear strength of RC beam-column joints using new ANN formulations. Structures 38:1191–1209, DOI: https://doi.org/10.1016/j.istruc.2022.02.046
Hawileh RA, Naser M, Zaidan W, Rasheed HA (2009) Modeling of insulated CFRP-strengthened reinforced concrete T-beam exposed to fire. Engineering Structures 31(12):3072–3079, DOI: https://doi.org/10.1016/j.engstruct.2009.08.008
Hosseinpour M, Sharif Y, Sharif H (2020) Neural network application for distortional buckling capacity assessment of castellated steel beams. Structures 27:1174–1183, DOI: https://doi.org/10.1016/j.istruc.2020.07.027
Kodur VKR, Bhatt PP (2018) A numerical approach for modeling response of fiber reinforced polymer strengthened concrete slabs exposed to fire. Composite Structures 187:226–240, DOI: https://doi.org/10.1016/j.compstruct.2017.12.051
Kotsovou GM, Cotsovos DM, Lagaros ND (2017) Assessment of RC exterior beam-column joints based on artificial neural networks and other methods. Engineering Structures 144:1–18, DOI: https://doi.org/10.1016/j.engstruct.2017.04.048
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11.2:431–441
Naser M, Abu-Lebdeh G, Hawileh R (2012) Analysis of RC T-beams strengthened with CFRP plates under fire loading using ANN. Construction and Building Materials 101(Part 1):30–38, DOI: https://doi.org/10.1016/j.conbuildmat.2012.07.001
Naderpour H, Kheyroddin A, Amiri G G (2010) Prediction of FRP-confined compressive strength of concrete using artificial neural networks. Composite Structures 92(12):2817–2829, DOI: https://doi.org/10.1016/j.compstruct.2010.04.008
Nikbin MI, Rahimi RS, Allahyari H (2017) A new empirical formula for prediction of fracture energy of concrete based on the artifcial neural network. Engineering Fracture Mechanics 186:466–482, DOI: https://doi.org/10.1016/j.engfracmech.2017.11.010
Panahi M, Zareei SA, Izadi A (2021) Flexural strengthening of reinforced concrete beams through externally bonded FRP sheets and near surface mounted FRP bars. Case Studies in Construction Materials 15:e00601, DOI: https://doi.org/10.1016/j.cscm.2021.e00601
Sun Z, Chen Y, Li X, Qin X, Wang H (2017) A bayesian regularized artificial neural network for adaptive optics forecasting. Optics Communications 382:519–527, DOI: https://doi.org/10.1016/j.optcom.2016.08.035
Thai H-T (2022) Machine learning for structural engineering: a state-of-the-art review. Structures 38:448–491, DOI: https://doi.org/10.1016/j.istruc.2022.02.003
Tran VL, Kim JK (2022) Revealing the nonlinear behavior of steel flush endplate connections using ANN-based hybrid models. Journal of Building Engineering 57:104878, DOI: https://doi.org/10.1016/j.jobe.2022.104878
Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility assessment in the hoa binh province of vietnam: A comparison of the levenbergmarquardt and bayesian regularized neural networks. Geomorphology 171–172:12–29, DOI: https://doi.org/10.1016/j.geomorph.2012.04.023
Tran V, Thai D, Kim S (2019) Application of ANN in predicting ACC of SCFST column. Composite Structures 228:111332, DOI: https://doi.org/10.1016/j.compstruct.2019.111332
Wu D, Huang H, Qiu S, Liu Y, Wu Y, Ren Y, Mou J (2022) Application of Bayesian regularization back propagation neural network in sensorless measurement of pump operational state. Energy Reports 8:3041–3050, DOI: https://doi.org/10.1016/j.egyr.2022.02.072
Xiang K, Wang GH (2013) Calculation of flexural strengthening of fire-damaged reinforced concrete beams with CFRP sheets. Procedia Engineering 52:446–452, DOI: https://doi.org/10.1016/j.proeng.2013.02.167
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This research was supported by Korea Electric Power Corporation (Grant number: R21XO01-33).
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Kang, SM., Kim, JK. Prediction of the Moment Capacity of FRP-Strengthened RC Beams Exposed to Fire Using ANNs. KSCE J Civ Eng 27, 3471–3483 (2023). https://doi.org/10.1007/s12205-023-0229-3
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DOI: https://doi.org/10.1007/s12205-023-0229-3