The Punching Shear Capacity Estimation of FRP- Strengthened RC Slabs Using Artificial Neural Network and Group Method of Data Handling

Document Type : Regular Paper

Author

Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

Abstract

Recently soft computing methods have been employed in most fields, especially in civil engineering, due to its high accuracy to predict the results and process information. Soft computing is the result of new scientific endeavors that make modeling, analysis, and, ultimately, the control of complex systems possible with greater ease and success. The essential methods of soft computing are fuzzy logic, artificial neural networks, and genetic algorithm. In this paper, using 74 valid experimental data, estimation of punching shear capacity of FRP-strengthened RC slabs using two powerful methods (artificial neural network and Group method of data handling) has been investigated. The maximum and minimum dimension of column cross-section, the effective height of slab, the compressive strength of concrete, modulus of elasticity of FRP bar, and the percentage of FRP bars were selected as input variables, and the punching shear capacity of the slab was selected as the output variable. Also, in order to investigate the effect of the variables mentioned above on the results, sensitivity analysis is conducted in both methods. Absolute Fraction of Variance for the two methods showed that the GMDH method had higher precision (1.73%) than the ANN method in the prediction of results.

Keywords

Main Subjects

References

[1]       Metwally IM. Prediction of punching shear capacities of two-way concrete slabs reinforced with FRP bars. HBRC J 2013;9:125–33. doi:10.1016/j.hbrcj.2013.05.009.
[2]       Hassan M, Ahmed EA, Benmokrane B. Punching shear behavior of two-way slabs reinforced with FRP shear reinforcement. J Compos Constr 2015. doi:10.1061/(ASCE)CC.1943-5614.0000493.
[3]       Durucan C, Anil Ö. Effect of opening size and location on the punching shear behavior of interior slab-column connections strengthened with CFRP strips. Eng Struct 2015. doi:10.1016/j.engstruct.2015.09.033.
[4]       Azimi A. GMDH-Network to Estimate the Punching Capacity of FRP-RC Slabs. Soft Comput Civ Eng 2017;1:86–92. doi:10.22115/scce.2017.48352.
[5]       Naderpour H, Rezazadeh Eidgahee D, Fakharian P, Rafiean AH, Kalantari SM. A new proposed approach for moment capacity estimation of ferrocement members using Group Method of Data Handling. Eng Sci Technol an Int J 2020;23:382–91. doi:10.1016/j.jestch.2019.05.013.
[6]       Rezazadeh Eidgahee D, Rafiean AH, Haddad A. A Novel Formulation for the Compressive Strength of IBP-Based Geopolymer Stabilized Clayey Soils Using ANN and GMDH-NN Approaches. Iran J Sci Technol Trans Civ Eng 2020;44:219–29. doi:10.1007/s40996-019-00263-1.
[7]       Rezazadeh Eidgahee D, Haddad A, Naderpour H. Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Sci Iran 2019;26:3233–44. doi:10.24200/sci.2018.5663.1408.
[8]       Naderpour H, Fakharian P, Rafiean AH, Yourtchi E. Estimation of the Shear Strength Capacity of Masonry Walls Improved with Fiber Reinforced Mortars (FRM) Using ANN-GMDH Approach. J Concr Struct Mater 2016;1:47–59. doi:10.30478/JCSM.2016.48988.
[9]       Hamed Akbarpour MA. Prediction of punching shear strength of two-way slabs using artificial neural network and adaptive neuro-fuzzy inference system. Neural Comput Appl 2016:1–12. doi:10.1007/s00521-016-2239-2.
[10]     Hassan NZ, Osman MA, El-Hashimy AM, Tantawy HK. Enhancement of punching shear strength of flat slabs using shear-band reinforcement. HBRC J 2018. doi:10.1016/j.hbrcj.2017.11.003.
[11]     Akhundzada H, Donchev T, Petkova D. Strengthening of slab-column connection against punching shear failure with CFRP laminates. Compos Struct 2019. doi:10.1016/j.compstruct.2018.09.076.
[12]     Marí A, Cladera A, Oller E, Bairán JM. A punching shear mechanical model for reinforced concrete flat slabs with and without shear reinforcement. Eng Struct 2018. doi:10.1016/j.engstruct.2018.03.079.
[13]     Hamdy M, Saafan M, Elwan SK, Elzeiny SM, Abdelrahman A. Punching Shear Behavior of RC Flat Slabs Strengthened with Steel Shear Bolts. Int J Curr Eng Technol 2018. doi:10.14741/ijcet/v.8.3.20.
[14]     Gokkus U, Yildirim M, Yilmazoglu A. Prediction of Concrete and Steel Materials Contained by Cantilever Retaining Wall by Modeling the Artificial Neural Networks. J Soft Comput Civ Eng 2018;2:47–61. doi:10.22115/scce.2018.137218.1078.
[15]     Wu X, Yu S, Xue S, Kang THK, Hwang HJ. Punching shear strength of UHPFRC-RC composite flat plates. Eng Struct 2019. doi:10.1016/j.engstruct.2019.01.099.
[16]     Naderpour H, Nagai K, Fakharian P, Haji M. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Compos Struct 2019;215:69–84. doi:10.1016/j.compstruct.2019.02.048.
[17]     Azizi R, Talaeitaba SB. Punching shear strengthening of flat slabs with CFRP on grooves (EBROG) and external rebars sticking in grooves. Int J Adv Struct Eng 2019. doi:10.1007/s40091-019-0218-4.
[18]     Darvishan E. Prediction of the Lateral Confinement Coefficient of The concrete Columns Confined by FRP using the Artificial Neural Network. Concr Res 2020;13:67–80. doi:10.22124/jcr.2020.12174.1335.
[19]     Fakharian P, Naderpour H, Haddad A, Rafiean AH, Rezazadeh ED. A Proposed Model for Compressive Strength Prediction of FRP-Confined Rectangular Columns in terms of Genetic Expression Programming (GEP). Concr Res 2018. doi:10.22124/jcr.2018.7162.1191.
[20]     Li X, Khademi F, Liu Y, Akbari M, Wang C, Bond PL, et al. Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion. J Environ Manage 2019;234:431–9. doi:10.1016/j.jenvman.2018.12.098.
[21]     Hasanzade-Inallu A, Zarfam P, Nikoo M. Modified imperialist competitive algorithm-based neural network to determine shear strength of concrete beams reinforced with FRP. J Cent South Univ 2019;26:3156–74. doi:10.1007/s11771-019-4243-z.
[22]     Naderpour H, Fakharian P, Hosseini F. Prediction of Behavior of FRP-Confined Circular Reinforced Concrete Columns using Artificial Neural Network. 8th Natl Conf Concr, Tehran, Iran: 2016. doi:10.13140/RG.2.2.11714.58568.
[23]     ACI 318-11. Building code requirements for structural concrete (ACI318-11). 2011.
[24]     British Standard. Structural use of concrete - Part 1. Code of practice for design and construction 1997:1–128.
[25]     Committee ACI 440. Guide for the Design and Construction of Concrete Reinforced with FRP Bars (ACI 440.1 R-06). Am Concr Institute, Detroit, Michigan 2006.
[26]     El-Ghandour AW, Pilakoutas K, Waldron P. Punching Shear Behavior of Fiber Reinforced Polymers Reinforced Concrete Flat Slabs: Experimental Study. J Compos Constr 2003;7:258–65. doi:10.1061/(ASCE)1090-0268(2003)7:3(258).
[27]     Matthys S, Taerwe L. Concrete slabs reinforced with FRP grids. II: Punching resistance. J Compos Constr 2000;4:154–61.
[28]     Ospina CE, Alexander SDB, Roger Cheng JJ. Erratum: Punching of Two-Way Concrete Slabs with Fiber-Reinforced Polymer Reinforcing Bars or Grids (ACI Structural Journal (September-October 2003) 100:5). ACI Struct J 2003;100:834.
[29]     El-Gamal S, El-Salakawy E, Benmokrane B. Behavior of concrete bridge deck slabs reinforced with fiber-reinforced polymer bars under concentrated loads. ACI Struct J 2005;102:727–35.
[30]     Banthia N, Al-Asaly M, Ma S. Behavior of concrete slabs reinforced with fiber-reinforced plastic grid. J Mater Civ Eng 1995;7:252–7.
[31]     Lee JH, Yoon YS, Cook WD, Mitchell D. Improving punching shear behavior of glass fiber-reinforced polymer reinforced slabs. ACI Struct J 2009;106:427–34.
[32]     Ahmad SH, Zia P, Yu TJ, Xie Y. Punching Shear Tests of Slabs Reinforced with 3-Dimensional Carbon Fiber Fabric. Concr Int 1994;16:36–41.
[33]     El-Salakawy E, Benmokrane B. Serviceability of concrete bridge deck slabs reinforced with fiber-reinforced polymer composite bars. ACI Struct J 2004;101:727–36. doi:10.14359/13395.
[34]     Rahman AH, Kingsley CY, Kobayashi K. Service and ultimate load behavior of bridge deck reinforced with carbon FRP grid. J Compos Constr 2000;4:16–23.
[35]     Hassan T, Abdelrahman  a, Tadros G, Rizkalla S. Fibre reinforced polymer reinforcing bars for bridge decks. Can J Civ Eng 2000;27:839–49. doi:10.1139/l99-098.
[36]     Hussein A, Rashid I, Benmokrane B. Two-way concrete slabs reinforced with GFRP bars. Adv Compos Mater Bridg Struct Proceeding 4th Int Conf Adv Compos Mater Bridg Struct CSCE, Calgary, Alta, Canada, July, 2004, p. 20–3.
[37]     Ayish M. Punching shear behavior of flat plates with fiber reinforced concrete. Proc Int Conf on, Compos Constr 2004.
[38]     Bouguerra K, Ahmed EA, El-Gamal S, Benmokrane B. Testing of full-scale concrete bridge deck slabs reinforced with fiber-reinforced polymer (FRP) bars. Constr Build Mater 2011;25:3956–65. doi:10.1016/j.conbuildmat.2011.04.028.
[39]     Ramzy Z, Mahmoud Z, Salma T. Punching behavior and strength of two-way concrete slab reinforced with glass-fiber reinforced polymer (GFRP) rebars. Struct Compos Infrastructures Appl Conf 2007.
[40]     Dulude C. Poinçonnement des dalles bidirectionnelles en béton armé d’armature de polymères renforcés de fibres de verre. Université de Sherbrooke; 2011.
[41]     Hassan M, Ahmed E, Benmokrane B. Punching-Shear Strength of Normal and High-Strength Two-Way Concrete Slabs Reinforced with GFRP Bars. J Compos Constr 2013;17:04013003. doi:10.1061/(ASCE)CC.1943-5614.0000424.
[42]     Nguyen-Minh L, Rovňák M. Punching shear resistance of interior GFRP reinforced slab-column connections. J Compos Constr 2012;17:2–13.
[43]     Flood I. Next generation artificial neural networks and their application to civil engineering. Work Eur Gr Intell Comput Eng, 2006, p. 206–21.
[44]     Kurnaz TF, Kaya Y. A novel ensemble model based on GMDH-type neural network for the prediction of CPT-based soil liquefaction. Environ Earth Sci 2019. doi:10.1007/s12665-019-8344-7.
[45]     Milne L. Feature selection using neural networks with contribution measures. Aust Conf Artif Intell AI’95, Citeseer; 1995, p. 1–8.

Files

Cited by

History

  • Receive Date: 04 May 2020
  • Revise Date: 22 July 2020
  • Accept Date: 05 October 2020

Share

How to cite

Statistics