Neural Computing and Applications

, Volume 31, Issue 7, pp 2085–2094 | Cite as

Development of prediction models for shear strength of SFRCB using a machine learning approach

  • Masoud Sarveghadi
  • Amir H. GandomiEmail author
  • Hamed Bolandi
  • Amir H. Alavi
Theory and Applications of Soft Computing Methods


In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.


SFRCB Multi-expression programming Shear strength Prediction 



The authors are thankful to Professor Marc O. Eberhard (University of Washington) for providing a part of the experimental database. The authors appreciate the support and stimulating discussions of Professor Mohammad Ghasem Sahab [Amirkabir University of Technology (Tehran Polytechnic)].


  1. 1.
    Chanh VN (2004) Steel fiber-reinforced concrete. Faculty of Civil Engineering Ho chi minh City University of Technology. Seminar Material, pp 108–116Google Scholar
  2. 2.
    Gandomi AH, Alavi AH, Yun GJ (2011) Nonlinear modeling of shear strength of SFRC beams using linear genetic programming. Struct Eng Mech 38(1):1–25CrossRefGoogle Scholar
  3. 3.
    Khuntia M, Stojadinovic B, Goel SC (1999) Shear strength of normal and high-strength fiber reinforced concrete beams without stirrups. ACI Struct J 96(2):282–289Google Scholar
  4. 4.
    Li VC, Ward R, Hamza AM (1992) Steel and synthetic fibers as shear reinforcement. ACI Mater J 89(5):499–508Google Scholar
  5. 5.
    Wang C (2006) Experimental investigation on behavior of steel fiber-reinforced concrete. MSc Thesis. University of Canterbury, New ZealandGoogle Scholar
  6. 6.
    Kwak YK, Eberhard MO, Kim WS, Kim J (2002) Shear strength of steel fiber-reinforced concrete beams without stirrups. ACI Struct J 99(4):530–538Google Scholar
  7. 7.
    Swamy RN, Jones R, Chiam ATP (1993) Influence of steel fibers on the shear resistance of lightweight concrete I-beams. ACI Struct J 90(1):103–114Google Scholar
  8. 8.
    Sharma AK (1986) Shear strength of steel fiber reinforced concrete beams. ACI J 83(4):624–628Google Scholar
  9. 9.
    Narayanan R, Darwish IYS (1987) Use of steel fibers as shear reinforcement. ACI Struct J 84(3):216–227Google Scholar
  10. 10.
    Ashour SA, Hasanain GS, Wafa FF (1992) Hear behavior of high-strength fiber reinforced concrete beams. ACI Struct J 89(2):176–184Google Scholar
  11. 11.
    Shin SW, Oh J, Ghosh SK (1994) Shear behavior of laboratory-sized high-strength concrete beams reinforced with bars and steel fibers. Fiber reinforced concrete developments and innovations, SP-142. American Concrete Institute, Farmington Hills, pp 181–200Google Scholar
  12. 12.
    Mansur MA, Ong KCG, Paramsivam P (1986) Shear strength of fibrous concrete beams without stirrups. J Struct Eng 112(9):2066–2079CrossRefGoogle Scholar
  13. 13.
    Tanyildizi H, Çevik A (2010) Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming. Constr Build Mater 24(12):2612–2618CrossRefGoogle Scholar
  14. 14.
    Ozbay E, Gesoglu M, Güneyisi E (2008) Empirical modeling of fresh and hardened properties of self-compacting concretes by genetic programming. Constr Build Mater 22(8):1831–1840CrossRefGoogle Scholar
  15. 15.
    Sarıdemir M (2010) Genetic programming approach for prediction of compressive strength of concretes containing rice husk ash. Constr Build Mater 24(10):1911–1919CrossRefGoogle Scholar
  16. 16.
    Alqedra MA, Ashour AF (2005) Prediction of shear capacity of single anchors located near a concrete edge using neural networks. Comput Struct 83(28–30):2495–2502CrossRefGoogle Scholar
  17. 17.
    Sakla SSS, Ashour AF (2005) Prediction of tensile capacity of single adhesive anchors using neural networks. Comput Struct 83(21–22):1792–1803CrossRefGoogle Scholar
  18. 18.
    Cabalar AF, Cevik A (2009) Modelling damping ratio and shear modulus of sand-mica mixtures using neural networks. Eng Geol 104:31–40CrossRefGoogle Scholar
  19. 19.
    Adhikary BB, Mutsuyoshi H (2006) Prediction of shear strength of steel fiber RC beams using neural networks. Constr Build Mater 20:801–811CrossRefGoogle Scholar
  20. 20.
    Ahn N, Jang H, Park DK (2007) Presumption of shear strength of steel fiber reinforced concrete beam using artificial neural network model. J Appl Polym Sci 103:2351–2358CrossRefGoogle Scholar
  21. 21.
    Koza J (1992) Genetic programming, on the programming of computers by means of natural selection. MIT Press, CambridgezbMATHGoogle Scholar
  22. 22.
    Sarıdemir M (2011) Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming. Exp Syst Appl 38(11):14257–14268Google Scholar
  23. 23.
    Azamathulla HMd, Guven A, Demir YK (2011) Linear genetic programming to scour below submerged pipeline. Ocean Eng 38(8):995–1000CrossRefGoogle Scholar
  24. 24.
    Alavi AH, Gandomi AH (2011) A robust data mining approach for formulation of geotechnical engineering systems. Eng Comput 28(3):242–274zbMATHCrossRefGoogle Scholar
  25. 25.
    Azamathulla HMd, Zahiri A (2012) Flow discharge prediction in compound channels using linear genetic programming. J Hydrol 454–455C:203–207Google Scholar
  26. 26.
    Oltean M, Dumitrescu D (2002) Multi expression programming. Technical report, UBB-01-2002, Babeş-Bolyai University, Cluj-Napoca, RomaniaGoogle Scholar
  27. 27.
    Oltean M, Grosan C (2003) A comparison of several linear genetic programming techniques. Complex Syst 14(4):1–29MathSciNetzbMATHGoogle Scholar
  28. 28.
    Alavi AH, Gandomi AH, Sahab MG, Gandomi M (2010) Multi expression programming: a new approach to formulation of soil classification. Eng Comput 26(2):111–118CrossRefGoogle Scholar
  29. 29.
    Gandomi AH, Alavi AH, Yun GJ (2011) Formulation of uplift suction caissons using multi expression programming. KSCE J Civil Eng 15(2):363–373CrossRefGoogle Scholar
  30. 30.
    Swamy RN, Mangat PS, Rao CVSK (1974) The mechanics of fiber reinforcement of cement matrices, fiber reinforced concrete, SP-44. American Concrete Institute, Farmington Hills, pp 1–28Google Scholar
  31. 31.
    Batson G, Jenkins E, Spatney R (1972) Steel fibers as shear reinforcement in beams. ACI J 69(10):640–644Google Scholar
  32. 32.
    Roberts TM, Ho NL (1982) Shear failure of deep fiber reinforced concrete beams. Int J Cement Comp Lightweight Concr 4(3):145–152CrossRefGoogle Scholar
  33. 33.
    Jindal RL (1984) Shear and moment capacities of steel fiber reinforced concrete beams. In: Hoff GC (ed) Fiber reinforced concrete, SP-81. American Concrete Institute, Farmington Hills, pp 1–16Google Scholar
  34. 34.
    Swamy RN, Bahia HM (1985) The effectiveness of steel fibers as shear reinforcement. Concr Int 7(3):35–40Google Scholar
  35. 35.
    Uomoto T, Weerarathe RK, Furukoshi H, Fujino H (1986) Shear strength of reinforced concrete beams with fibre reinforcement. In: Proceedings of the third international on RILEM symposium on developments in fibre reinforced cement and concrete, Sheffield, 1986. RILEM Technical Committee 49-TFR, Sheffield University Press Unit, Sheffield, 553–562Google Scholar
  36. 36.
    Kadir MRA, Saeed JA (1986) Shear strength of fibre reinforced concrete beams. J Eng Technol 4(3):98–112Google Scholar
  37. 37.
    Kaushik SK, Gupta VK, Tarafdar NK (1987) Behavior of fiber reinforced concrete beams in shear. In: Proceedings of the international symposium on fiber reinforced concrete, Madras, India, pp 1.133–1.149Google Scholar
  38. 38.
    Murty DSR, Venkatacharyulu T (1987) Fiber reinforced concrete beams subjected to shear force. In: Proceedings of the international symposium on fiber reinforced concrete, Madras, India, pp 1125–1132Google Scholar
  39. 39.
    Lim TY, Paramasivam P, Lee SL (1987) Shear and moment capacity of reinforced steel-fiber-concrete beams. Mag Concr Res 39(140):148–160CrossRefGoogle Scholar
  40. 40.
    Narayanan R, Darwish IYS (1988) Fiber concrete deep beams in shear. ACI Struct J 85(2):141–149Google Scholar
  41. 41.
    Tan KH, Murugappan K, Paramasivam P (1992) Shear behavior of steel fiber reinforced concrete beams. ACI Struct J 89(6):3–11Google Scholar
  42. 42.
    Imam M, Vandewalle L, Mortelmans F (1994) Shear capacity of steel fiber high-strength concrete beams. In: Malhotra VM (ed) High-performance concrete, SP-149. American Concrete Institute, Farmington Hills, pp 227–241Google Scholar
  43. 43.
    Adebar P, Mindess SSt, Pierre D, Olund B (1997) Shear tests of fiber concrete beams without stirrups. ACI Struct J 94(1):68–76Google Scholar
  44. 44.
    Oh BH, Lim DH, Yoo SW, Kim ES (1998) Shear behavior and shear analysis of reinforced concrete beams containing steel fibers. Mag Concr Res 50(4):283–291CrossRefGoogle Scholar
  45. 45.
    Casanova P, Rossi P (1999) High-strength concrete beams submitted to shear: steel fibers versus stirrups. In: Banthia N, MacDonald C, Tatnall P (eds) Structural applications of fiber reinforced concrete, SP-182. American Concrete Institute, Farmington Hills, pp 53–67Google Scholar
  46. 46.
    Noghabai K (2000) Beams of fibrous concrete in shear and bending: experiment and model. J Struct Eng ASCE 126(2):243–251CrossRefGoogle Scholar
  47. 47.
    Cucchiara C, Mendola LL, Papia M (2004) Effectiveness of stirrups and steel fibres as shear reinforcement. Cem Concr Comp 26:777–786CrossRefGoogle Scholar
  48. 48.
    Oltean M (2004) Multi expression programming source code.
  49. 49.
    Gandomi AH, Alavi AH, Mousavi M, Tabatabaei SM (2011) A hybrid computational approach to derive new ground-motion attenuation models. Eng Appl Artif Intell 24(4):717–732CrossRefGoogle Scholar
  50. 50.
    Smith GN (1986) Probability and statistics in civil engineering. Collins, LondonGoogle Scholar
  51. 51.
    Golbraikh A, Tropsha A (2002) Beware of q2. J Mol Graph Model 20(4):269–276CrossRefGoogle Scholar
  52. 52.
    Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313CrossRefGoogle Scholar
  53. 53.
    Dimopoulos C, Zalzala AMS (2001) Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv Eng Softw 32(6):489–498zbMATHCrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  • Masoud Sarveghadi
    • 1
  • Amir H. Gandomi
    • 2
    Email author
  • Hamed Bolandi
    • 3
  • Amir H. Alavi
    • 4
  1. 1.Department of Civil Engineering, Kashmar BranchIslamic Azad UniversityKashmarIran
  2. 2.BEACON Center for the Study of Evolution in ActionMichigan State UniversityEast LansingUSA
  3. 3.Department of Civil Engineering, Bandar Abbas BranchIslamic Azad UniversityBandar AbbasIran
  4. 4.Department of Civil and Environmental EngineeringMichigan State UniversityEast LansingUSA

Personalised recommendations