Skip to main content
SpringerLink
Log in

Nonlinear modeling for bar bond stress using dynamical self-adjusted harmony search optimization

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Design code provisions for reinforced concrete are often based on empirical relations resulting from simple statistical treatments of experimental data. Hence, they may provide inaccurate results for predicting complex structural behavior. In the present study, novel nonlinear regression for prediction of the reinforcing bar development length is developed using dynamical self-adjusted harmony search optimization. The nonlinear mathematical relations are regressed using 534 results of simple pullout tests on short unit bar lengths. A novel bi-nonlinear expression is proposed, and its predictive capability outperformed that of design code formulas such as the ACI 318-14, ACI 408R-03, and Eurocode 2 along with other existing empirical models. A parametric study was conducted to explore the sensitivity of the proposed models to influential input parameters. It was found that the new model offers a powerful predictive tool for reinforcing bar bond strength which differs from that of existing models that assume unrealistic uniform bond stress along the rebar. This flexible and data-intensive model could be further scrutinized for consideration in future design code revisions and enhancements.

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

Similar content being viewed by others

References

  1. Alam MS, Moni M, Tesfamariam S (2012) Seismic overstrength and ductility of concrete buildings reinforced with superelastic shape memory alloy rebar. Eng Struct 34:8–20. https://doi.org/10.1016/j.engstruct.2011.08.030

    Article  Google Scholar 

  2. Saleem MA, Mirmiran A, Xia J, Mackie K (2013) Development length of high-strength steel rebar in ultrahigh performance concrete. J Mater Civ Eng 25:991–998. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000571

    Article  Google Scholar 

  3. Wight JK, MacGreger JG (2012) Reinforcedd concrete: mechanics and design. Pearson, New York, p 1157

    Google Scholar 

  4. ACI Committee 318 (2014) Building code requirements for structural concrete (ACI 318-14) and commentary (ACI 318R-14). American Concrete Institute, Farmington Hills, p 519

    Google Scholar 

  5. ACI-ASCE Committee 408 (2003) Bond and development of straight reinforcing bars in Tension (ACI 408R-03). American Concrete Institute, Farmington Hills, p 49

    Google Scholar 

  6. BS EN 1992:1:2004. Eurocode 2: design of concrete structures. British Standards Institution, London, pp 225

  7. Hwang HJ, Park HG, Yi WJ (2017) Nonuniform bond stress distribution model for evaluation of bar development length. ACI Struct J 114:839–849. https://doi.org/10.14359/51689446

    Article  Google Scholar 

  8. Yaseen ZM, Keshtegar B, Hwang HJ, Nehdi ML (2019) Predicting reinforcing bar development length using polynomial chaos expansions. Eng Struct 195:524–535

    Article  Google Scholar 

  9. Keshtegar B, Sadeghian P, Gholampour A, Ozbakkaloglu T (2017) Nonlinear modeling of ultimate strength and strain of FRP-confined concrete using chaos control method. Compos Struct 163:423–431

    Article  Google Scholar 

  10. Kowsar R, Keshtegar B, Marey MA, Miyamoto A (2017) An autoregressive logistic model to predict the reciprocal effects of oviductal fluid components on in vitro spermophagy by neutrophils in cattle. Sci Rep 7(1):4482

    Article  Google Scholar 

  11. Kowsar R, Keshtegar B, Miyamoto A (2019) Understanding the hidden relations between pro-and anti-inflammatory cytokine genes in bovine oviduct epithelium using a multilayer response surface method. Sci Rep 9(1):3189

    Article  Google Scholar 

  12. Keshtegar B, Piri J, Kisi O (2016) A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method. Comput Electron Agric 127:120–130

    Article  Google Scholar 

  13. Keshtegar B, Etedali S (2018) Nonlinear mathematical modeling and optimum design of tuned mass dampers using adaptive dynamic harmony search algorithm. Struct Control Health Monit 25(7):e2163

    Article  Google Scholar 

  14. Keshtegar B, Ozbakkaloglu T, Gholampour A (2017) Modeling the behavior of FRP-confined concrete using dynamic harmony search algorithm. Eng Comput 33(3):415–430

    Article  Google Scholar 

  15. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulations 76:60–68

    Article  Google Scholar 

  16. Keshtegar B, Hao P, Wang Y, Hu Q (2018) An adaptive response surface method and Gaussian global-best harmony search algorithm for optimization of aircraft stiffened panels. Appl Soft Comput 66:196–207

    Article  Google Scholar 

  17. Keshtegar B, Hao P, Wang Y, Li Y (2017) Optimum design of aircraft panels based on adaptive dynamic harmony search. Thin-Walled Struct 118:37–45

    Article  Google Scholar 

  18. Ouyang HB, Gao LQ, Li S, Kong XY (2015) Improved novel global harmony search with a new relaxation method for reliability optimization problems. Inf Sci 305:14–55

    Article  Google Scholar 

  19. Kattan A, Abdullah R (2013) A dynamic self-adaptive harmony search algorithm for continuous optimization problems. Appl Math Comput 219:8542–8567

    MathSciNet  MATH  Google Scholar 

  20. Kang SL, Zong WG (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798

    Article  Google Scholar 

  21. Geem ZW, Kim JH, Loganathan GV (2015) Harmony search optimization: application to pipe network design. Int J Model Simul 22:125–133

    Article  Google Scholar 

  22. Razfar MR, Zinati RF, Haghshenas M (2011) Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm. Int J Adv Manuf Technol 52(5–8):487–495

    Article  Google Scholar 

  23. Lee A, Geem ZW, Suh KD (2016) Determination of optimal initial weights of an artificial neural network by using the harmony search algorithm: application to breakwater armor stones. Appl Sci 6(6):164

    Article  Google Scholar 

  24. Jaddi NS, Abdullah S (2017) A cooperative-competitive master-slave global-best harmony search for ANN optimization and water-quality prediction. Appl Soft Comput 51:209–224

    Article  Google Scholar 

  25. Kulluk S, Ozbakir L, Baykasoglu A (2011) Self-adaptive global best harmony search algorithm for training neural networks. Proc Comput Sci 3:282–286

    Article  Google Scholar 

  26. Lee JH, Yoon YS (2009) Modified harmony search algorithm and neural networks for concrete mix proportion design. J Comput Civ Eng 23(1):57–61

    Article  Google Scholar 

  27. Afan HA, Keshtegar B, Mohtar WHMW, El-Shafie A (2017) Harmonize input selection for sediment transport prediction. J Hydrol 552:366–375

    Article  Google Scholar 

  28. Keshtegar B, Seghier MEAB (2018) Modified response surface method basis harmony search to predict the burst pressure of corroded pipelines. Eng Fail Anal 89:177–199

    Article  Google Scholar 

  29. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250

    Article  Google Scholar 

  30. Keshtegar B, Heddam S (2018) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput Appl 30(10):2995–3006

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Western University, London, ON, Canada

    Moncef L. Nehdi

  2. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Behrooz Keshtegar

  3. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Behrooz Keshtegar

  4. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Shun-Peng Zhu

Authors
  1. Moncef L. Nehdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Behrooz Keshtegar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shun-Peng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behrooz Keshtegar.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nehdi, M.L., Keshtegar, B. & Zhu, SP. Nonlinear modeling for bar bond stress using dynamical self-adjusted harmony search optimization. Engineering with Computers 37, 409–420 (2021). https://doi.org/10.1007/s00366-019-00831-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-019-00831-z

Keywords

Search

Navigation