Skip to main content

Advertisement

SpringerLink
Log in

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

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

Abstract

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R2) during the testing phase (R2 = 0.95), whereas the other comparable models generated relatively lower values of R2 ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

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

Similar content being viewed by others

References

  1. Mohamed K, Farghaly AS, Benmokrane B (2017) Effect of vertical and horizontal web reinforcement on the strength and deformation of concrete deep beams reinforced with GFRP bars. J Struct Eng 143:4017079

    Article  Google Scholar 

  2. Cao J, Bloodworth AG, Xu M (2019) Efficient two-way shear grillage model solution for bridge RC four-pile caps under wall loading. J Bridg Eng 24:4019071

    Article  Google Scholar 

  3. Shahnewaz M, Rteil A, Alam MS (2020) Shear strength of reinforced concrete deep beams–a review with improved model by genetic algorithm and reliability analysis. In: Structures. Elsevier, pp 494–508

  4. Demir A, Caglar N, Ozturk H (2019) Parameters affecting diagonal cracking behavior of reinforced concrete deep beams. Eng Struct 184:217–231

    Article  Google Scholar 

  5. Díaz RAS, Nova SJS, da Silva MCAT et al (2020) Reliability analysis of shear strength of reinforced concrete deep beams using NLFEA. Eng Struct 203:109760

    Article  Google Scholar 

  6. Tan KH, Weng LW, Teng S (1997) A strut-and-tie model for deep beams subjected to combined top-and-bottom loading. Struct Eng 75(13)

  7. Park J, Kuchma D (2007) Strut-and-tie model analysis for strength prediction of deep beams. ACI Struct J 104:657

    Google Scholar 

  8. Tang CY, Tan KH (2004) Interactive mechanical model for shear strength of deep beams. J Struct Eng 130:1534–1544

    Article  Google Scholar 

  9. Lezgy-Nazargah M (2020) A four-variable global–local shear deformation theory for the analysis of deep curved laminated composite beams. Acta Mech 1–32

  10. Pal M, Deswal S (2011) Support vector regression based shear strength modelling of deep beams. Comput Struct. https://doi.org/10.1016/j.compstruc.2011.03.005

    Article  Google Scholar 

  11. Chou J-S, Ngo N-T, Pham A-D (2015) Shear strength prediction in reinforced concrete deep beams using nature-inspired metaheuristic support vector regression. J Comput Civ Eng 30:4015002

    Article  Google Scholar 

  12. Mansour MY, Dicleli M, Lee JY, Zhang J (2004) Predicting the shear strength of reinforced concrete beams using artificial neural networks. Eng Struct 26:781–799. https://doi.org/10.1016/j.engstruct.2004.01.011

    Article  Google Scholar 

  13. Gandomi AH, Alavi AH, Shadmehri DM, Sahab MG (2013) An empirical model for shear capacity of RC deep beams using genetic-simulated annealing. Arch Civ Mech Eng 13:354–369

    Article  Google Scholar 

  14. Cheng M-Y, Prayogo D, Wu Y-W (2013) Novel genetic algorithm-based evolutionary support vector machine for optimizing high-performance concrete mixture. J Comput Civ Eng 28:6014003

    Article  Google Scholar 

  15. Cheng M-Y, Cao M-T (2014) Evolutionary multivariate adaptive regression splines for estimating shear strength in reinforced-concrete deep beams. Eng Appl Artif Intell 28:86–96

    Article  Google Scholar 

  16. Oh J-K, Shin S-W (2001) Shear strength of reinforced high-strength concrete deep beams. Struct J 98:164–173

    Google Scholar 

  17. ACI (2011) 318–11: building code requirements for structural concrete. MI Am Concr Inst, Farmingt Hills, p 505

    Google Scholar 

  18. Association CS (2004) Design of concrete structures. Canadian Standards Association, Mississauga

    Google Scholar 

  19. Arup O (1977) The design of deep beams in reinforced concrete. Construction Industry Research and Information Association

  20. CSA (1994) Design of concrete structures: structures (design)—a national standard of Canada. CAN-A23 3–94

  21. 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. https://doi.org/10.1016/j.scient.2012.02.009

    Article  Google Scholar 

  22. Cheng M-Y, Firdausi PM, Prayogo D (2014) High-performance concrete compressive strength prediction using genetic weighted pyramid operation tree (GWPOT). Eng Appl Artif Intell 29:104–113

    Article  Google Scholar 

  23. Moosazadeh S, Namazi E, Aghababaei H et al (2019) Prediction of building damage induced by tunnelling through an optimized artificial neural network. Eng Comput 35:579–591

    Article  Google Scholar 

  24. Bui DT, Nhu V-H, Hoang N-D (2018) Prediction of soil compression coefficient for urban housing project using novel integration machine learning approach of swarm intelligence and multi-layer perceptron neural network. Adv Eng Inform 38:593–604

    Article  Google Scholar 

  25. Keshtegar B, Bagheri M, Yaseen ZM (2019) Shear strength of steel fiber-unconfined reinforced concrete beam simulation: application of novel intelligent model. Compos Struct 212:230–242

  26. Ashrafian A, Shokri F, Amiri MJT et al (2020) Compressive strength of foamed cellular lightweight concrete simulation: new development of hybrid artificial intelligence model. Constr Build Mater 230:117048

    Article  Google Scholar 

  27. Adhikary BB, Mutsuyoshi H (2006) Prediction of shear strength of steel fiber RC beams using neural networks. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2005.01.047

    Article  Google Scholar 

  28. Gou J, Fan ZW, Wang C et al (2016) A minimum-of-maximum relative error support vector machine for simultaneous reverse prediction of concrete components. Comput Struct 172:59–70. https://doi.org/10.1016/j.compstruc.2016.05.003

    Article  Google Scholar 

  29. Chen XL, Fu JP, Yao JL, Gan JF (2018) Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Eng Comput. https://doi.org/10.1007/s00366-017-0547-5

    Article  Google Scholar 

  30. Deng F, He Y, Zhou S et al (2018) Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 175:562–569. https://doi.org/10.1016/j.conbuildmat.2018.04.169

    Article  Google Scholar 

  31. Dung CV (2019) Autonomous concrete crack detection using deep fully convolutional neural network. Autom Constr 99:52–58

    Article  Google Scholar 

  32. Sanad A, Saka MP (2001) Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. J Struct Eng 127:818–828

    Article  Google Scholar 

  33. Mohammadhassani M, Nezamabadi-Pour H, Jumaat M et al (2013) Application of the ANFIS model in deflection prediction of concrete deep beam. Struct Eng Mech 45:319–332

    Article  Google Scholar 

  34. Kulkrni KS, Kim D-K, Sekar SK, Samui P (2011) Model of least square support vector machine (LSSVM) for prediction of fracture parameters of concrete. Int J Concr Struct Mater 5:29–33. https://doi.org/10.4334/IJCSM.2011.5.1.029

    Article  Google Scholar 

  35. Tang HS, Xue ST, Chen R, Sato T (2006) Online weighted LS-SVM for hysteretic structural system identification. Eng Struct 28:1728–1735. https://doi.org/10.1016/j.engstruct.2006.03.008

    Article  Google Scholar 

  36. Yan K, Shi C (2010) Prediction of elastic modulus of normal and high strength concrete by support vector machine. Constr Build Mater 24:1479–1485. https://doi.org/10.1016/j.conbuildmat.2010.01.006

    Article  Google Scholar 

  37. Das M, Dey AK (2019) Prediction of bearing capacity of stone columns placed in soft clay using SVR model. Arab J Sci Eng 44:4681–4691

    Article  Google Scholar 

  38. Chen W, Hasanipanah M, Rad HN, Armaghani DJ, Tahir MM (2019) A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Eng Comput 1–17

  39. Clark AP (1951) Diagonal tension in reinforced concrete beams. J Proc 48(10):145–156

  40. Kong F-K, Robins PJ, Cole DF (1970) Web reinforcement effects on deep beams. J Proc 67(12):1010–1018

  41. Smith KN, Vantsiotis AS (1982) Shear strength of deep beams. J Proc 79(3):201–213

  42. Anderson NS, Ramirez JA (1989) Detailing of stirrup reinforcement. Struct J 86:507–515

    Google Scholar 

  43. Tan K-H, Kong F-K, Teng S, Guan L (1995) High-strength concrete deep beams with effective span and shear span variations. Struct J 92:395–405

    Google Scholar 

  44. Naik U, Kute S (2013) Span-to-depth ratio effect on shear strength of steel fiber-reinforced high-strength concrete deep beams using ANN model. Int J Adv Struct Eng 5:29. https://doi.org/10.1186/2008-6695-5-29

    Article  Google Scholar 

  45. Aguilar G, Matamoros AB, Parra-Montesinos G et al (2002) Experimental evaluation of design procedures for shear strength of deep reinfoced concrete beams. American Concrete Institute

  46. Quintero-Febres CG, Parra-Montesinos G, Wight JK (2006) Strength of struts in deep concrete members designed using strut-and-tie method. ACI Struct J 103:577

    Google Scholar 

  47. Abd AM, Abd SM (2017) Modelling the strength of lightweight foamed concrete using support vector machine (SVM). Case Stud Constr Mater 6:8–15. https://doi.org/10.1016/j.cscm.2016.11.002

    Article  Google Scholar 

  48. Yaseen ZM, Tran MT, Kim S et al (2018) Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: a new approach. Eng Struct 177:244–255. https://doi.org/10.1016/j.engstruct.2018.09.074

    Article  Google Scholar 

  49. Raghavendra NS, Deka PC (2014) Support vector machine applications in the field of hydrology: a review. Appl Soft Comput 19:372–386. https://doi.org/10.1016/j.asoc.2014.02.002

    Article  Google Scholar 

  50. Vapnik VN (2000) The nature of statistical learning theory, second. Springer, New York

    Book  Google Scholar 

  51. Cortes C, Vapnik V (1995) Support vector machine. Mach Learn 20:273–297

    MATH  Google Scholar 

  52. Vapnik VN (1998) Statistical learning theory

  53. Wu KP, De WS (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognit 42:710–717. https://doi.org/10.1016/j.patcog.2008.08.030

    Article  MATH  Google Scholar 

  54. Chatterjee S, Sarkar S, Hore S et al (2017) Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm. Struct Eng Mech 63:429–438

    Google Scholar 

  55. Yan F, Lin Z (2016) New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm. Compos Part B Eng 92:420–433

    Article  Google Scholar 

  56. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge

    Book  Google Scholar 

  57. Zhang CY, Wei JS, Wang Z et al (2019) Creep-based reliability evaluation of turbine blade-tip clearance with novel neural network regression. Materials (Basel). https://doi.org/10.3390/ma12213552

    Article  Google Scholar 

  58. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133. https://doi.org/10.1007/BF02478259

    Article  MathSciNet  MATH  Google Scholar 

  59. Diop L, Bodian A, Djaman K et al (2018) The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River. Environ Earth Sci 77:182

    Article  Google Scholar 

  60. Ghorbani MA, Khatibi R, Karimi V et al (2018) Learning from multiple models using artificial intelligence to improve model prediction accuracies: application to river flows. Water Resour Manag. https://doi.org/10.1007/s11269-018-2038-x

    Article  Google Scholar 

  61. Alwanas AAH, Al-Musawi AA, Salih SQ et al (2019) Load-carrying capacity and mode failure simulation of beam-column joint connection: application of self-tuning machine learning model. Eng Struct 194:220–229. https://doi.org/10.1016/j.engstruct.2019.05.048

    Article  Google Scholar 

  62. Almonti D, Baiocco G, Tagliaferri V, Ucciardello N (2019) Artificial neural network in fibres length prediction for high precision control of cellulose refining. Materials (Basel). https://doi.org/10.3390/ma12223730

    Article  Google Scholar 

  63. Bhagat SK, Tung TM, Yaseen ZM (2019) Development of artificial intelligence for modeling wastewater heavy metal removal: state of the art, application assessment and possible future research. J Clean Prod 250:119473

    Article  Google Scholar 

  64. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer, New York

    Book  Google Scholar 

  65. Zhou J, Shi X, Li X (2016) Utilizing gradient boosted machine for the prediction of damage to residential structures owing to blasting vibrations of open pit mining. J Vib Control 22:3986–3997

    Article  Google Scholar 

  66. Schapire RE (2003) The boosting approach to machine learning: an overview. In: Nonlinear estimation and classification. Springer, pp 149–171

  67. Zhou J, Li X, Mitri HS (2016) Classification of rockburst in underground projects: comparison of ten supervised learning methods. J Comput Civ Eng 30:4016003

    Article  Google Scholar 

  68. Londhe RS (2011) Shear strength analysis and prediction of reinforced concrete transfer beams in high-rise buildings. Struct Eng Mech 37:39

    Article  Google Scholar 

  69. Ashour AF, Alvarez LF, Toropov VV (2003) Empirical modelling of shear strength of RC deep beams by genetic programming. Comput Struct. https://doi.org/10.1016/S0045-7949(02)00437-6

    Article  Google Scholar 

  70. El-Sayed AK (2006) Concrete contribution to the shear resistance of FRP-reinforced concrete beams (Doctoral dissertation, Ph. D. thesis, University of Sherbrooke, Sherbrooke, Quebec, Canada)

  71. Yang K-H, Chung H-S, Lee E-T, Eun H-C (2003) Shear characteristics of high-strength concrete deep beams without shear reinforcements. Eng Struct 25:1343–1352

    Article  Google Scholar 

  72. Mau ST, Hsu TSTC (1989) Formula for the shear strength of deep beams. Struct J 86:516–523

    Google Scholar 

  73. Hofmann M, Klinkenberg R (2016) RapidMiner: data mining use cases and business analytics applications. CRC Press, Boca Raton

    Book  Google Scholar 

  74. Hameed MM, AlOmar MK (2020) Prediction of compressive strength of high-performance concrete: hybrid artificial intelligence technique. In: Al-Jumeily D, Lisitsa A, Khalaf MI (eds) Applied computing to support industry: innovation and technology. Springer International Publishing, Cham, pp 323–335

    Chapter  Google Scholar 

  75. AlOmar MK, Hameed MM, AlSaadi MA (2020) Multi hours ahead prediction of surface ozone gas concentration: robust artificial intelligence approach. Atmos Pollut Res. https://doi.org/10.1016/j.apr.2020.06.024

    Article  Google Scholar 

  76. Prayogo D, Cheng M-Y, Wu Y-W, Tran D-H (2019) Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Eng Comput. https://doi.org/10.1007/s00366-019-00753-w

    Article  Google Scholar 

Download references

Acknowledgement

The authors appreciate and acknowledge the support received by the Key Research and Development Program in Shaanxi Province (2019GY-131), for conducting this research. Also, we are very much thankful for the respected reviewers and editors for their excellent constructive comments.

Author information

Authors and Affiliations

  1. School of Computer Science, Baoji University of Arts and Sciences, Baoji, 721007, China

    Guangnan Zhang

  2. Civil Engineering Department, College of Engineering, University of Diyala, Baquba, Iraq

    Zainab Hasan Ali

  3. Department of Mechanical Engineering, Collage of Mechanical Engineering Technology, Benghazi, Libya

    Mohammed Suleman Aldlemy

  4. Building and Construction Engineering Techniques Department, Al-Mussaib Technical College, Al-Furat Al-Awast Technical University, 51009, Babylon, Iraq

    Mohamed H. Mussa

  5. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Sinan Q. Salih

  6. Computer Science Department, College of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq

    Sinan Q. Salih

  7. Department of Civil Engineering, Al-Maaref University College, Ramadi, Iraq

    Mohammed Majeed Hameed

  8. Department of Civil Engineering, Al-Mustaqbal University College, Babil, Iraq

    Zainab S. Al-Khafaji

  9. Al-Furat Al-Awsat Distribution Foundation, Ministry of Oil, Babylon, Iraq

    Zainab S. Al-Khafaji

  10. Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Zaher Mundher Yaseen

Authors
  1. Guangnan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zainab Hasan Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed Suleman Aldlemy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed H. Mussa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sinan Q. Salih
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohammed Majeed Hameed
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zainab S. Al-Khafaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zaher Mundher Yaseen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zaher Mundher Yaseen.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, G., Ali, Z.H., Aldlemy, M.S. et al. Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model. Engineering with Computers 38 (Suppl 1), 15–28 (2022). https://doi.org/10.1007/s00366-020-01137-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-020-01137-1

Keywords

Search

Navigation