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Cost-based optimization of shear capacity in fiber reinforced concrete beams using machine learning

  • Nassif, Nadia (Department of Civil and Environmental Engineering, College of Engineering, University of Sharjah) ;
  • Al-Sadoon, Zaid A. (Department of Civil and Environmental Engineering, College of Engineering, University of Sharjah) ;
  • Hamad, Khaled (Department of Civil and Environmental Engineering, College of Engineering, University of Sharjah) ;
  • Altoubat, Salah (Department of Civil and Environmental Engineering, College of Engineering, University of Sharjah)
  • Received : 2022.03.02
  • Accepted : 2022.06.27
  • Published : 2022.09.10

Abstract

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R2) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

Keywords

References

  1. Adebar, P., Mindess, S., Pierre, D.S. and Olund, B. (1997), "Shear tests of fiber concrete beams without stirrups", Struct. J., 94(1), 68-76.
  2. Ahmad, S.H., Hino, S., Chung, W. and Xie, Y. (1995), "An experimental technique for obtaining controlled diagonal tension failure of shear critical reinforced concrete beams", Mater. Struct., 28(1), 8-15. https://doi.org/10.1007/bf02473287.
  3. Ahmad, S.H., Khaloo, A.R. and Poveda, A. (1986), "Shear capacity of reinforced high-strength concrete beams", J. Proc., 83(2), 297-305.
  4. Ahmadi, M., Kheyroddin, A., Dalvand, A. and Kioumarsi, M. (2020), "New empirical approach for determining nominal shear capacity of steel fiber reinforced concrete beams", Constr. Build. Mater., 234, 117293. https://doi.org/10.1016/j.conbuildmat.2019.117293.
  5. Alotaibi, E., Mostafa, O., Nassif, N., Omar, M. and Arab, M.G. (2021), "Prediction of punching shear capacity for fiberreinforced concrete slabs using neuro-nomographs constructed by machine learning", J. Struct. Eng., 147(6), 04021075. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003041.
  6. Ashour, S.A., Hasanain, G.S. and Wafa, F.F. (1992), "Shear behavior of high-strength fiber reinforced concrete beams", Struct. J., 89(2), 176-184.
  7. Capitol Steel LLC (2002), Reinforcing Steel Bars Price List. https://capitolsteel.com.ph/rebar-price-list/
  8. Chajec, A. and Sadowski, L. (2020), "The effect of steel and polypropylene fibers on the properties of horizontally formed concrete", Mater., 13(24), 5827. https://doi.org/10.3390/ma13245827.
  9. Cucchiara, C., La Mendola, L. and Papia, M. (2004), "Effectiveness of stirrups and steel fibres as shear reinforcement", Cement Concrete Compos., 26(7), 777-786. https://doi.org/10.1016/j.cemconcomp.2003.07.001.
  10. Cybenko, G. (1989), "Approximation by superpositions of a sigmoidal function", Math. Control Signal. Syst., 2(4), 303-314. https://doi.org/10.1007/BF02551274.
  11. Demakos, C.B., Repapis, C.C. and Drivas, D.P. (2021), "Experimental investigation of shear strength for steel fibre reinforced concrete beams", Open Constr. Build. Technol. J., 15(1), 81-92. https://doi.org/10.2174/1874836802115010081.
  12. Dinh, H.H. (2009), "Shear behavior of steel fiber reinforced concrete beams without stirrup reinforcement", PhD Dissertation, University of Michigan, Michigan, USA.
  13. Funahashi, K.I. (1989), "On the approximate realization of continuous mappings by neural networks", Neur. Network., 2(3), 183-192. https://doi.org/10.1016/0893-6080(89)90003-8.
  14. Gandomi, A.H., Alavi, A.H. and Yun, G.J. (2011), "Nonlinear modeling of shear strength of SFRC beams using linear genetic programming", Struct. Eng. Mech., 38(1), 1-25. https://doi.org/10.12989/sem.2011.38.1.001.
  15. Hamad, K., Khalil, M.A. and Shanableh, A. (2017), "Modeling roadway traffic noise in a hot climate using artificial neural networks", Transp. Res. Part D: Transp. Environ., 53, 161-177. https://doi.org/10.1016/j.trd.2017.04.014.
  16. Homaifar, A., Qi, C.X. and Lai, S.H. (1994), "Constrained optimization via genetic algorithms", Simul., 62(4), 242-253. https://doi.org/10.1177/003754979406200405.
  17. Hossain, K., Gladson, L.R. and Anwar, M.S. (2017), "Modeling shear strength of medium-to ultra-high-strength steel fiberreinforced concrete beams using artificial neural network", Neur. Comput. Appl., 28(1), 1119-1130. https://doi.org/10.1007/s00521-016-2417-2.
  18. Islam, M.S. (2021), "Simplified shear-strength prediction models for steel-fibre-reinforced concrete beams", Proc. Inst. Civil Eng.-Constr. Mater., 174(2), 88-100. https://doi.org/10.1680/jcoma.16.00073.
  19. Islam, M.S. and Alam, S. (2013), "Principal component and multiple regression analysis for steel fiber reinforced concrete (SFRC) beams", Int. J. Concrete Struct. Mater., 7(4), 303-317. https://doi.org/10.1007/s40069-013-0059-7.
  20. Jameran, A., Ibrahim, I.S., Yazan, S.H.S. and Rahim, S.N.A. (2015), "Mechanical properties of steel-polypropylene fibre reinforced concrete under elevated temperature", Procedia Eng., 125, 818-824. https://doi.org/10.1016/j.proeng.2015.11.146.
  21. Kara, I.F. (2013), "Empirical modeling of shear strength of steel fiber reinforced concrete beams by gene expression programming", Neur. Comput. Appl., 23(3), 823-834. https://doi.org/10.1007/s00521-012-0999-x.
  22. Kim, T., Tae, S. and Roh, S. (2013), "Assessment of the CO2 emission and cost reduction performance of a low-carbonemission concrete mix design using an optimal mix design system", Renew. Sustain. Energy Rev., 25, 729-741. https://doi.org/10.1016/j.rser.2013.05.013.
  23. Kurda, R., De Brito, J. and Silvestre, J.D. (2018), "Combined economic and mechanical performance optimization of recycled aggregate concrete with high volume of fly ash", Appl. Sci., 8(7), 1189. https://doi.org/10.3390/app8071189.
  24. Kwon, S.J. and Wang, X.Y. (2019), "Optimization of the mixture design of low-CO2 high-strength concrete containing silica fume", Adv. Civil Eng., 2019, Article ID 7168703. https://doi.org/10.1155/2019/7168703.
  25. Lantsoght, E.O. (2019), "How do steel fibers improve the shear capacity of reinforced concrete beams without stirrups?", Compos. Part B: Eng., 175, 107079. https://doi.org/10.1016/j.compositesb.2019.107079.
  26. Lim, T.Y., Paramasivam, P. and Lee, S.L. (1987), "Shear and moment capacity of reinforced steel-fibre-concrete beams", Mag. Concrete Res., 39(140), 148-160. https://doi.org/10.1680/macr.1987.39.140.148.
  27. Lin, A.L. (2013), "Prediction of shear strength of fiber reinforced concrete beam of intermediate shear span", PhD Dissertation, College of Engineering, Department of Civil Engineering, National Taiwan University, Taiwan.
  28. Ly, H.B., Le, T.T., Vu, H.L.T., Tran, V.Q., Le, L.M. and Pham, B.T. (2020), "Computational hybrid machine learning based prediction of shear capacity for steel fiber reinforced concrete beams", Sustain., 12(7), 2709. https://doi.org/10.3390/su12072709.
  29. Minelli, F. and Plizzari, G.A. (2013), "On the effectiveness of steel fibers as shear reinforcement", ACI Struct. J., 110(3). 379.
  30. Mohammed, T.U., Ahmed, T., Apurbo, S.M., Mallick, T.A., Shahriar, F., Munim, A. and Awal, M.A. (2017), "Influence of chemical admixtures on fresh and hardened properties of prolonged mixed concrete", Adv. Mater. Sci. Eng., 2017, Article ID 9187627. https://doi.org/10.1155/2017/9187627.
  31. Noghabai, K. (2000), "Beams of fibrous concrete in shear and bending: Experiment and model", J. Struct. Eng., 126(2), 243-251. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:2(243).
  32. Omidinasab, F. and Goodarzimehr, V. (2020), "A hybrid particle swarm optimization and genetic algorithm for truss structures with discrete variables", J. Appl. Comput. Mech., 6(3), 593-604. https://doi.org/10.22055/JACM.2019.28992.1531.
  33. Perera, S.V.T.J. and Mutsuyoshi, H. (2013), "Shear capacity of reinforced high-strength concrete beams without web reinforcement", Proceedings of the Thirteenth East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-13), September.
  34. Sarveghadi, M., Gandomi, A.H., Bolandi, H. and Alavi, A.H. (2019), "Development of prediction models for shear strength of SFRCB using a machine learning approach", Neur. Comput. Appl., 31(7), 2085-2094. https://doi.org/10.1007/s00521-015-1997-6.
  35. Scott, D.A., Long, W.R., Moser, R.D., Green, B.H., O'Daniel, J.L. and Williams, B.A. (2015), "Impact of steel fiber size and shape on the mechanical properties of ultra-high performance concrete", Engineer Research and Development Center Vicksburg Geotechnical and Structures Lab.
  36. Shahin, M.A., Maier, H.R. and Jaksa, M.B. (2002), "Predicting settlement of shallow foundations using neural networks", J. Geotech. Geoenviron. Eng., 128(9), 785-793. https://doi.org/10.1061/(ASCE)1090-0241(2002)128:9(785)
  37. Shahnewaz, M. and Tannert, M.S.A.T. (2016), "Shear strength prediction of steel fiber reinforced concrete beams from genetic programming and its sensitivity analysis", FRC: The Modern Landscape BEFIB 2016 9th Rilem International Symposium on Fiber Reinforced Concrete.
  38. Slater, E., Moni, M. and Alam, M.S. (2012), "Predicting the shear strength of steel fiber reinforced concrete beams", Constr. Build. Mater., 26(1), 423-436. https://doi.org/10.1016/j.conbuildmat.2011.06.042.
  39. Varaee, H. and Nedushan, B. (2011), "Minimum cost design of concrete slabs using particle swarm optimization with time varying acceleration coefficients", World Appl. Sci. J., 13(12), 2484-2494.
  40. Wang, X.Y. (2019), "Effect of carbon pricing on optimal mix design of sustainable high-strength concrete", Sustain., 11(20), 5827. https://doi.org/10.3390/su11205827.
  41. Yaseen, Z.M., Tran, M.T., Kim, S., Bakhshpoori, T. and Deo, R.C. (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.