Skip to main content
SpringerLink
Log in

Hybrid intelligence modeling for estimating shear strength of FRP reinforced concrete members

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The corrosion problem in the conventional steel reinforcement in concrete structures has diverted the researchers to explore alternative materials. As a substitute to replace the traditional steel bars in reinforced concrete structures, innovative reinforcement such as fiber reinforced polymer (FRP) rebars has been suggested. Consequently, different codes and guidelines have been proposed for forecasting the shear strength of FRP reinforced members using traditional empirical methods and neural networks. The current paper concentrates on the development of a hybrid intelligence model, namely an artificial neural network articulated with a Bayesian optimization algorithm (ANN-BOA) for estimating the shear strength of these types of members without stirrups. Totally, 216 specimens, collected from the literature, were used in this analysis. The input parameters of the model were beam depth, the ratio of shear span and depth, effective reinforcement ratio, and concrete strength. The ANN hyperparameters (viz. neuron numbers in the hidden layer, learning rate) have been tuned automatically to get the best predictions. The estimated shear strengths are compared with the recent design provisions of Japan (JSCE), UK (BISE), Italy (CNR-DT 203), Canada (CHBDC, CSA S806), and the USA (ACI 440.1R). The results were also compared with a similar ANN model. It was observed that the predicted results using the proposed method are better than those of the other methods in terms of some statistical as well as performance measuring parameters with a maximum Pearson correlation coefficient (R) value of 0.97. These values are higher than the other investigated methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Islam MS, Alam S (2013) Principal component and multiple regression analysis for steel fiber reinforced concrete (SFRC) beams. Int J Concr Struct Mater 7:303–317. https://doi.org/10.1007/s40069-013-0059-7

    Article  Google Scholar 

  2. Islam MS, Ahmed SJ (2018) Influence of jute fiber on concrete properties. Constr Build Mater 189:768–776. https://doi.org/10.1016/j.conbuildmat.2018.09.048

    Article  Google Scholar 

  3. Kotynia R, Szczech D, Kaszubska M (2017) Bond Behavior of GRFP Bars to Concrete in Beam Test. Proc Eng 193:401–408

    Article  Google Scholar 

  4. ACI440.1R (2015) Guide for the design and construction of structural concrete reinforced with fiber-reinforced polymer bars (ACI440.1R-15). Farmington Hills

  5. ASCE (1998) Recent approaches to shear design of structural concrete (ASCE ACI-445). J Struct Eng 124:1375–1417. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:12(1375)

    Article  Google Scholar 

  6. Yost JR, Gross SP, Dinehart DW (2001) Shear strength of normal strength concrete beams reinforced with deformed GFRP bars. J Compos Constr 5:268–275. https://doi.org/10.1061/(ASCE)1090-0268(2001)5:4(268)

    Article  Google Scholar 

  7. El-Sayed AK, El-Salakawy EF, Benmokrane B (2006) Shear capacity of high-strength concrete beams reinforced with fiber-reinforced polymer bars. ACI Struct J. https://doi.org/10.14359/15316

    Article  Google Scholar 

  8. Bentz EC, Vecchio FJ, Collins MP (2006) Simplified modified compression field theory for calculating shear strength of reinforced concrete elements. ACI Struct J 103:614–624. https://doi.org/10.14359/16438

    Article  Google Scholar 

  9. ASCE (1998) Recent approaches to shear design of structural concrete. ACI-ASCE 98. J Struct Eng 124:1375–1417. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:12(1375)

    Article  Google Scholar 

  10. Michaluk R, Rizkalla S, Tadros G, Benmokrane B (1998) Flexural behavior of one-way concrete slabs reinforced by fiber reinforced plastic reinforcement. ACI Struct J 95:353–365

    Google Scholar 

  11. Deitz DH, Gesund H, Harik IE (1999) One-way slabs reinforced with glass fiber reinforced polymer reinforcing bars. ACI Symp Publ. https://doi.org/10.14359/5629

    Article  Google Scholar 

  12. Razaqpur AG, Isgor OB (2006) proposed shear design method for frp-reinforced concrete members without stirrups. ACI Struct J 103:93–102. https://doi.org/10.14359/15090

    Article  Google Scholar 

  13. Tureyen AK, Frosch RJ (2002) Shear tests of FRP-reinforced concrete beams without stirrups. ACI Struct J. https://doi.org/10.14359/12111

    Article  Google Scholar 

  14. JSCE (1997) Recommendation for design and construction of concrete structures using continuous fiber reinforcing materials. Japan Society of Civil Engineers, Tokyo

    Google Scholar 

  15. BISE (1999) Interim guidance on the design of reinforced concrete structures using fibre composite reinforcement. BISE, London

    Google Scholar 

  16. CNR-DT-203 (2006) Guide for the design and construction of concrete structures reinforced with fiber-reinforced polymer bars. Cnr-Dt-203/2006 39

  17. CAN/CSA-S6 (2014) Canadian highway bridge design code

  18. CSA S806 (2012) Design and construction of building structures with fibre-reinforced polymers. CSA S806–12. Rexdale, Ontario, Canada

  19. ACI 440.1R (2006) Guide for the design and construction of structural concrete reinforced with fiber-reinforced polymer bars. ACI 440.1R-06. Farmington Hills

  20. Nguyen QH, Ly HB, Nguyen TA et al (2021) Investigation of ANN architecture for predicting shear strength of fiber reinforcement bars concrete beams. PLoS ONE 16:1–22. https://doi.org/10.1371/journal.pone.0247391

    Article  Google Scholar 

  21. Pham BT, Nguyen MD, Van DD et al (2019) Development of artificial intelligence models for the prediction of compression coefficient of soil: an application of Monte Carlo sensitivity analysis. Sci Total Environ 679:172–184. https://doi.org/10.1016/j.scitotenv.2019.05.061

    Article  Google Scholar 

  22. Ly HB, Le LM, Duong HT et al (2019) Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections. Appl Sci. https://doi.org/10.3390/app9112258

    Article  Google Scholar 

  23. Van Dao D, Ly HB, Trinh SH et al (2019) Artificial intelligence approaches for prediction of compressive strength of geopolymer concrete. Materials (Basel). https://doi.org/10.3390/ma12060983

    Article  Google Scholar 

  24. Van DD, Jaafari A, Bayat M et al (2020) A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. CATENA 188:104451. https://doi.org/10.1016/j.catena.2019.104451

    Article  Google Scholar 

  25. Mashrei MA (2011) Prediction of the shear strength of concrete beams reinforced with fiber reinforced polymer bars using artificial neural networks model. Thi Qar Univ J Eng Sci 2:45–63

    Google Scholar 

  26. Bashir R, Ashour A (2012) Neural network modelling for shear strength of concrete members reinforced with FRP bars. Compos Part B Eng 43:3198–3207. https://doi.org/10.1016/j.compositesb.2012.04.011

    Article  Google Scholar 

  27. Lee S, Lee C (2014) Prediction of shear strength of FRP-reinforced concrete flexural members without stirrups using artificial neural networks. Eng Struct 61:99–112. https://doi.org/10.1016/j.engstruct.2014.01.001

    Article  Google Scholar 

  28. Yavuz G (2019) Determining the shear strength of FRP-RC beams using soft computing and code methods. Comput Concr 23:49–60. https://doi.org/10.12989/cac.2019.23.1.049

    Article  Google Scholar 

  29. Naderpour H, Poursaeidi O, Ahmadi M (2018) Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks. Meas J Int Meas Confed 126:299–308. https://doi.org/10.1016/j.measurement.2018.05.051

    Article  Google Scholar 

  30. Naderpour H, Haji M, Mirrashid M (2020) Shear capacity estimation of FRP-reinforced concrete beams using computational intelligence. Structures 28:321–328. https://doi.org/10.1016/j.istruc.2020.08.076

    Article  Google Scholar 

  31. Amani J, Moeini R (2012) Prediction of shear strength of reinforced concrete beams using adaptive neuro-fuzzy inference system and artificial neural network. Sci Iran 19:242–248. https://doi.org/10.1016/j.scient.2012.02.009

    Article  Google Scholar 

  32. Jumaa GB, Yousif AR (2018) Predicting shear capacity of FRP-reinforced concrete beams without stirrups by artificial neural networks, gene expression programming, and regression analysis. Adv Eng Softw. https://doi.org/10.1155/2018/5157824

    Article  Google Scholar 

  33. Alam MS, Gazder U (2020) Shear strength prediction of FRP reinforced concrete members using generalized regression neural network. Neural Comput Appl 32:6151–6158. https://doi.org/10.1007/s00521-019-04107-x

    Article  Google Scholar 

  34. Al-Musawi AA, Alwanas AAH, Salih SQ et al (2020) Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model. Eng Comput 36:1–11. https://doi.org/10.1007/s00366-018-0681-8

    Article  Google Scholar 

  35. Wu J, Chen X-Y, Zhang H et al (2019) Hyperparameter optimization for machine learning models based on bayesian optimization. J Electron Sci Technol 17:26–40. https://doi.org/10.11989/JEST.1674-862X.80904120

    Article  Google Scholar 

  36. Alkhrdaji T, Wideman MA, Belarbi A, Nanni A (2001) Shear strength of GFRP RC beams and slabs. In: Figueiras J, Juvandes L, Faria R (eds) Proceedings 3rd international conference on composites in construction, Porto, Portugal. A.A. Balkema Publishers, Netherlands, pp 409–414

  37. Massam L (2001) The behavior of GFRP reinforced concrete beams in shear. University of Toronto, Toronto

    Google Scholar 

  38. Gross SP, Yost JR, Dinehart DW, Svensen E, Liu N (2003) Shear strength of normal and high strength concrete beams reinforced with gfrp bars. In: International conference on high performance materials in bridges, Hawai, USA. https://doi.org/10.1061/40691(2003)38

  39. Matta F, El-Sayed A, Nanni A, Benmokrane B (2013) Size effect on concrete shear strength in beams reinforced with fiber-reinforced polymer bars. ACI Struct J 110:617–628

    Google Scholar 

  40. Tariq M, Newhook J (2003) Shear testing of FRP reinforced concrete without transverse reinforcement. Proc Annu Conf Can Soc Civ Eng 2003:1330–1339

    Google Scholar 

  41. Razaqpur AG, Isgor BO, Greenaway S, Selley A (2004) Concrete contribution to the shear resistance of fiber reinforced polymer reinforced concrete members. J Compos Constr 8:452–460. https://doi.org/10.1061/(ASCE)1090-0268(2004)8:5(452)

    Article  Google Scholar 

  42. Zhang B, Masmoudi R, Benmokrane B (2004) Behaviour of one-way concrete slabs reinforced with CFRP grid reinforcements. Constr Build Mater 18:625–635. https://doi.org/10.1016/j.conbuildmat.2004.04.007

    Article  Google Scholar 

  43. Ashour AF (2005) Flexural and shear capacities of concrete beams reinforced with GFRP bars. Constr Build Mater 20:1005–1015. https://doi.org/10.1016/j.conbuildmat.2005.06.023

    Article  Google Scholar 

  44. El-Sayed AK, El-Salakawy EF, Benmokrane B (2006) Shear strength of FRP-reinforced concrete beams without transverse reinforcement. ACI Struct J 103:235–243. https://doi.org/10.14359/15181

    Article  Google Scholar 

  45. El-Sayed A, El-Salakawy E, Benmokrane B, El-Sayed AK, El-Salakawy EF, Benmokrane B (2005) Shear strength of one-way concrete slabs reinforced with fiber-reinforced polymer composite bars. J Compos Constr 9:147–157. https://doi.org/10.1061/(ASCE)1090-0268(2005)9:2(147)

    Article  Google Scholar 

  46. El-Sayed AK, El-Salakawy EF, Benmokrane B (2006) Shear capacity of high-strength concrete beams reinforced with FRP bars. ACI Struct J 103:383–389. https://doi.org/10.14359/15316

    Article  Google Scholar 

  47. Guadagnini M, Pilakoutas K, Waldron P (2006) Shear resistance of FRP RC beams: experimental study. J Compos Constr 10:464–473. https://doi.org/10.1061/(ASCE)1090-0268(2006)10:6(464)

    Article  Google Scholar 

  48. Steiner S, El-Sayed AK, Benmokrane B et al (2008) Shear Strength of Large-Size Concrete Beams Reinforced with Glass FRP Bars. In: CSCE (ed) 5th International conference on advance composites materials in bridges and structures. CSCE, Winnipeg

    Google Scholar 

  49. Alam MS, Hussein A (2013) Size effect on shear strength of frp reinforced concrete beams without stirrups. J Compos Constr 17:507–516. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000346

    Article  Google Scholar 

  50. Alam MS, Hussein A (2011) Experimental investigation on the effect of longitudinal reinforcement on shear strength of fibre reinforced polymer reinforced concrete beams. Can J Civ Eng. https://doi.org/10.1139/L10-126

    Article  Google Scholar 

  51. Alam MS, Hussein A (2012) Effect of member depth on shear strength of high-strength fiber-reinforced polymer-reinforced concrete beams. J Compos Constr. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000248

    Article  Google Scholar 

  52. Bentz EC, Massam L, Collins MP, Bentz EC, Massam L, Collins MP (2010) Shear strength of large concrete members with FRP reinforcement. J Compos Constr 14:637–646. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000108

    Article  Google Scholar 

  53. Olivito RS, Zuccarello FA (2010) On the shear behaviour of concrete beams reinforced by carbon fibre-reinforced polymer bars: an experimental investigation by means of acoustic emission technique. Strain 46:470–481. https://doi.org/10.1111/j.1475-1305.2009.00699.x

    Article  Google Scholar 

  54. Zeidan M, Barakat MA, Mahmoud Z, Khalifa A (2011) Evaluation of concrete shear strength for FRP reinforced beams. In: Structures congress 2011—Proceedings of the 2011 Structures congress. pp 1816–1826. https://doi.org/10.1061/41171(401)158

  55. Ashour AF, Kara IF (2014) Size effect on shear strength of FRP reinforced concrete beams. Compos Part B Eng 60:612–620. https://doi.org/10.1016/j.compositesb.2013.12.002

    Article  Google Scholar 

  56. Kim CH, Jang HS (2014) Concrete shear strength of normal and lightweight concrete beams reinforced with FRP bars. J Compos Constr 18:1–9. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000440

    Article  Google Scholar 

  57. Grieef S (1996) GFRP Dowel bars for concrete pavement. MSc Thesis. University of Manitoba

  58. Kotsovos MD, Pavlovic MN (1998) Ultimate limit-state design of concrete structures: a new approach. Thomas Telford Ltd, London

    Book  Google Scholar 

  59. Jain AK, Chandrasekaran B (1982) 39 Dimensionality and sample size considerations in pattern recognition practice. Handb Stat 2:835–855

    Article  Google Scholar 

  60. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13:252–264. https://doi.org/10.1109/34.75512

    Article  Google Scholar 

  61. Mockus J (1989) Global optimization and the bayesian approach. D. Reidel Publishing Company, Dordrecht

    Book  Google Scholar 

  62. Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Proceedings of the 25th international conference on neural information processing systems, pp 2951–2959

  63. Shahriari B, Swersky K, Wang Z et al (2016) Taking the human out of the loop: a review of Bayesian optimization. Proc IEEE 104:148–175

    Article  Google Scholar 

  64. Rasmussen CE (2006) Gaussian processes for machine learning: book webpage. MIT Press, Cambridge

    Google Scholar 

  65. Kumar A, Luo J, Bennett GF (1993) Statistical evaluation of lower flammability distance (LFD) using four hazardous release models. Process Saf Prog 12:1–11. https://doi.org/10.1002/prs.680120103

    Article  Google Scholar 

  66. Kumar A, Bellam NK, Sud A (1999) Performance of an industrial source complex model: predicting long-term concentrations in an urban area. Environ Prog 18:93–100. https://doi.org/10.1002/ep.670180213

    Article  Google Scholar 

  67. Imohamed A, Trendy T, Samad AAA, Mohamad N (2014) Diagonal shear cracks and size effect in concrete beams reinforced with glass fiber reinforced polymer (GFRP) Bars. Appl Mech Mater 621:113–119. https://doi.org/10.4028/www.scientific.net/amm.621.113

    Article  Google Scholar 

  68. Cholostiakow S, Di Benedetti M, Pilakoutas K, Guadagnini M (2019) Effect of beam depth on shear behavior of FRP RC beams. J Compos Constr. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000914

    Article  Google Scholar 

  69. Johnson DT, Sheikh SA (2016) Experimental investigation of glass fiber-reinforced polymer-reinforced normal-strength concrete beams. ACI Struct J 113:1165–1174. https://doi.org/10.14359/51689017

    Article  Google Scholar 

  70. Tottori S, Wakui H (1993) Shear capacity of RC and PC beams using FRP reinforcement. Aci Sp 138:615–631. https://doi.org/10.14359/3944

    Article  Google Scholar 

Download references

Acknowledgements

The resources provided by the authors’ Universities are gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, University of Bahrain, Zallaq, Bahrain

    Md. Shah Alam & Mohammad S. Islam

  2. Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

    N. Sultana

  3. Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallaq, Bahrain

    S. M. Zakir Hossain

Authors
  1. Md. Shah Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Sultana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. M. Zakir Hossain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad S. Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Md. Shah Alam.

Ethics declarations

Conflict of interest

The authors have no relevant interest to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 23 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alam, M.S., Sultana, N., Hossain, S.M.Z. et al. Hybrid intelligence modeling for estimating shear strength of FRP reinforced concrete members. Neural Comput & Applic 34, 7069–7079 (2022). https://doi.org/10.1007/s00521-021-06791-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06791-0

Keywords

Search

Navigation