Farahnaki, R., Azimi, A. (2018). An Equation to Determine the Ultimate Flexural Load of RC Beams Strengthened with CFRP Laminates. Soft Computing in Civil Engineering, (), 86-95. doi: 10.22115/scce.2018.136500.1076

Reza Farahnaki; Alla Azimi. "An Equation to Determine the Ultimate Flexural Load of RC Beams Strengthened with CFRP Laminates". Soft Computing in Civil Engineering, , , 2018, 86-95. doi: 10.22115/scce.2018.136500.1076

Farahnaki, R., Azimi, A. (2018). 'An Equation to Determine the Ultimate Flexural Load of RC Beams Strengthened with CFRP Laminates', Soft Computing in Civil Engineering, (), pp. 86-95. doi: 10.22115/scce.2018.136500.1076

Farahnaki, R., Azimi, A. An Equation to Determine the Ultimate Flexural Load of RC Beams Strengthened with CFRP Laminates. Soft Computing in Civil Engineering, 2018; (): 86-95. doi: 10.22115/scce.2018.136500.1076

An Equation to Determine the Ultimate Flexural Load of RC Beams Strengthened with CFRP Laminates

^{1}The University of Wollongong, Wollongong, Australia

^{2}Department of Civil Engineering, University of Birmingham, Birmingham, UK

Receive Date: 19 June 2018,
Revise Date: 29 September 2018,
Accept Date: 31 October 2018

Abstract

In this paper, a new relationship is presented for determining the ultimate flexural load of reinforced concrete beams strengthened with CFRP laminates. An artificial neural network with a suitable performance was used to estimate this equation. First, a collection of laboratory results including 83 data was collected from valid references. This database was then divided into three groups of 51, 16, and 16, which were used to train, validation, and test the proposed equation, respectively. The final model had eleven inputs including concrete compressive strength, width of beam, effective depth, area of tension reinforcement, area of compression reinforcement, yield strength of steel, modulus of elasticity of steel, modulus of elasticity of CFRP sheet, width of CFRP sheet, total thickness of CFRP sheets and, length of CFRP sheet, which were applied to the network to determine the ultimate flexural load as the output of the model. The obtained results from the proposed relationship showed that it was able to use as a predictive equation for the considered target.

[1] Seo SY, Choi KB, Kwon YS. Retrofit Capacity of Near-Surface-Mounted RC Beam by using FRP Plate. J Korea Inst Struct Maint Insp 2012;16:18–26. doi:10.11112/jksmi.2012.16.1.018.

[2] Dolan CW, Swanson D. Development of flexural capacity of a FRP prestressed beam with vertically distributed tendons. Compos Part B Eng 2002;33:1–6. doi:10.1016/S1359-8368(01)00053-1.

[3] Kheyroddin A, Mirrashid M, Arshadi H. An Investigation on the Behavior of Concrete Cores in Suspended Tall Buildings. Iran J Sci Technol Trans Civ Eng 2017;41:383–8. doi:10.1007/s40996-017-0075-y.

[4] Qu HC, Wu CQ, Chen LL. Numerical Analysis on the Load-Carrying Capacity for the FRP Reinforced Four-Point Bending Concrete Beam. Adv Mater Res 2011;287–290:1130–4. doi:10.4028/www.scientific.net/AMR.287-290.1130.

[5] Xu X. Calculation Method and Analysis of Bearing Capacity of FRP Rebar Concrete Beam. ICTE 2011, Reston, VA: American Society of Civil Engineers; 2011, p. 1572–7. doi:10.1061/41184(419)260.

[6] Zatloukal J, Konvalinka P. Moment Capacity of FRP Reinforced Concrete Beam Assessment Based on Centerline Geometry. Appl Mech Mater 2013;486:211–6. doi:10.4028/www.scientific.net/AMM.486.211.

[7] Jafari M, Mirrashid M, Vahidnia A. Prediction of chloride penetration in the concrete containing magnetite aggregates by Adaptive Neural Fuzzy Inference System (ANFIS). 7th Internatinal Symp. Adv. Sci. Technol. (5thsastech), Bandare Abbas, Iran, 2013.

[8] Mirrashid M. Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Nat Hazards 2014;74:1577–93. doi:10.1007/s11069-014-1264-7.

[9] Mirrashid M. Comparison Study of Soft Computing Approaches for Estimation of the Non-Ductile RC Joint Shear Strength. Soft Comput Civ Eng 2017;1:12–28. doi:10.22115/scce.2017.46318.

[10] Mirrashid M, Bigdeli S. Genetic Algorithm for Prediction the Compressive Strength of Mortar Containing Wollastonite. 1st Natl. Congr. Counstruction Eng. Proj. Assessment, Gorgan, Iran, 2014.

[11] Mirrashid M, Givehchi M, Miri M, Madandoust R. Performance investigation of neuro-fuzzy system for earthquake prediction. Asian J Civ Eng 2016;17:213–23.

[12] Mirrashid M, Jafari M, Akhlaghi A, Vahidnia A. Prediction of compressive strength of concrete containing magnetite aggregates using Adaptive Neural Fuzzy Inference System (ANFIS) n.d.

[13] Naderpour H, Mirrashid M. Application of Soft Computing to Reinforced Concrete Beams Strengthened with Fibre Reinforced Polymers: A State-of-the-Art Review. Comput Tech Civ Struct Eng n.d.:305–23.

[14] Naderpour H, Mirrashid M. Compressive Strength of Mortars Admixed with Wollastonite and Microsilica. Mater Sci Forum 2017;890:415–8. doi:10.4028/www.scientific.net/MSF.890.415.

[15] Naderpour H, Mirrashid M. Ultimate Capacity Prediction of Concrete Slabs Reinforced with FRP Bars. 3rd Int. 7th Natl. Conf. Mod. Mater. Struct. Civ. Eng. Bu-Ali Sina Univ. Hamedan, IRAN, 2018.

[16] Naderpour H, Mirrashid M. Application of group method of data handling to Estimate the Shear Strength of RC Beams Reinforced with FRP Bars. 3rd Int. 7th Natl. Conf. Mod. Mater. Struct. Civ. Eng. Bu-Ali Sina Univ. Hamedan, IRAN, 2018.

[17] Naderpour H, Mirrashid M. Shear Strength Prediction of RC Beams Using Adaptive Neuro-Fuzzy Inference System. Sci Iran 2018:0–0. doi:10.24200/sci.2018.50308.1624.

[18] Naderpour H, Mirrashid M. An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals. J Build Eng 2018;19:205–15. doi:10.1016/j.jobe.2018.05.012.

[19] Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.

[20] Naderpour H, Vahdani R, Mirrashid M. Soft Computing Research in Structural Control by Mass Damper (A review paper). 4st Int. Conf. Struct. Eng. Tehran, Iran, 2018.

[21] HAJELA P, BERKE L. Neurobiological Computational Models in Structural Analysis and Design. 31st Struct. Struct. Dyn. Mater. Conf., Reston, Virigina: American Institute of Aeronautics and Astronautics; 1990. doi:10.2514/6.1990-1133.

[22] Saadatmanesh H, Ehsani MR. RC Beams Strengthened with GFRP Plates. I: Experimental Study. J Struct Eng 1991;117:3417–33. doi:10.1061/(ASCE)0733-9445(1991)117:11(3417).

[23] C. Allen Ross Joseph W. Tedesco, and Mary L. Hughes DMJ. Strengthening of Reinforced Concrete Beams with Externally Bonded Composite Laminates. Struct J n.d.;96. doi:10.14359/612.

[24] Ni H-G, Wang J-Z. Prediction of compressive strength of concrete by neural networks. Cem Concr Res 2000;30:1245–50. doi:10.1016/S0008-8846(00)00345-8.

[25] Du Béton FI. Externally bonded FRP reinforcement for RC structures. Bulletin 2001;14:138.

[26] Sanad A, Saka MP. Prediction of Ultimate Shear Strength of Reinforced-Concrete Deep Beams Using Neural Networks. J Struct Eng 2001;127:818–28. doi:10.1061/(ASCE)0733-9445(2001)127:7(818).

[27] Dong Y, Zhao M, Ansari F. Failure characteristics of reinforced concrete beams repaired with CFRP composites. Strain 2002;304:12–7.

[28] Dai JG, Ueda T, Sato Y, Ito T. Flexural strengthening of RC beams using externally bonded FRP sheets through flexible adhesive bonding. Int. Symp. Bond Behav. FRP Struct. (BBFS 2005), Hong Kong, 2005, p. 7–9.

[29] Ebead UA, Marzouk H. Tension-stiffening model for FRP-strenghened RC concrete two-way slabs. Mater Struct 2005;38:193–200. doi:10.1007/BF02479344.

[30] Kotynia R. Debonding failures of RC beams strengthened with externally bonded strips. Proc. Int. Symp. Bond Behav. FRP Struct. (BBFS 2005), 2005.

[31] Lundqvist J, Nordin H, Täljsten B, Olofsson T. Numerical analysis of concrete beams strengthened with CFRP : a study of anchorage lengths. Int Symp Bond Behav FRP Struct 07/12/2005 - 09/12/2005 2005:239–46.

[32] Maalej M, Leong KS. Effect of beam size and FRP thickness on interfacial shear stress concentration and failure mode of FRP-strengthened beams. Compos Sci Technol 2005;65:1148–58. doi:10.1016/j.compscitech.2004.11.010.

[33] Coronado CA, Lopez MM. Sensitivity analysis of reinforced concrete beams strengthened with FRP laminates. Cem Concr Compos 2006;28:102–14. doi:10.1016/j.cemconcomp.2005.07.005.

[34] Reeve BZ. Effect of adhesive stiffness and CFRP geometry on the behavior of externally bonded CFRP retrofit measures subject to monotonic loads 2006.

[35] Abdalla JA, Elsanosi A, Abdelwahab A. Modeling and simulation of shear resistance of R/C beams using artificial neural network. J Franklin Inst 2007;344:741–56. doi:10.1016/j.jfranklin.2005.12.005.

[36] Esfahani MR, Kianoush MR, Tajari AR. Flexural behaviour of reinforced concrete beams strengthened by CFRP sheets. Eng Struct 2007;29:2428–44. doi:10.1016/j.engstruct.2006.12.008.

[37] Neagoe CA. Concrete beams reinforced with CFRP laminates 2011.