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Determination of shear strength of steel fiber RC beams: application of data-intelligence models

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Abstract

Accurate prediction of shear strength of structural engineering components can yield a magnificent information modeling and predesign process. This paper aims to determine the shear strength of steel fiber reinforced concrete beams using the application of data-intelligence models namely hybrid artificial neural network integrated with particle swarm optimization. For the considered data-intelligence models, the input matrix attribute is one of the central element in attaining accurate predictive model. Hence, various input attributes are constructed to model the shear strength “as a targeted variable”. The modeling is initiated using historical published researches steel fiber reinforced concrete beams information. Seven variables are used as input attribute combination including reinforcement ratio (ρ%), concrete compressive strength (f′c), fiber factor (F1), volume percentage of fiber (Vf), fiber length to diameter ratio (lf =ld) effective depth (d), and shear span-to-strength ratio (a/d), while the shear strength (Ss) is the output of the matrix. The best network structure obtained using the network having ten nodes and one hidden layer. The final results obtained indicated that the hybrid predictive model of ANN-PSO can be used efficiently in the prediction of the shear strength of fiber reinforced concrete beams. In more representable details, the hybrid model attained the values of root mean square error and correlation coefficient 0.567 and 0.82, respectively.

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References

  1. Zollo R F. Fiber-reinforced concrete: an overview after 30 years of development. Cement and Concrete Composites, 1997, 19(2): 107–122

    Article  Google Scholar 

  2. Park K, Paulino G H, Roesler J. Cohesive fracture model for functionally graded fiber reinforced concrete. Cement and Concrete Research, 2010, 40(6): 956–965

    Article  Google Scholar 

  3. Park S H, Kim D J, Ryu G S, Koh K T. Tensile behavior of ultra high performance hybrid fiber reinforced concrete. Cement and Concrete Composites, 2012, 34(2): 172–184

    Article  Google Scholar 

  4. Graybeal B A. Compressive behavior of ultra-high-performance fiber-reinforced concrete. ACI Materials Journal, 2007, 104(2): 146–152

    Google Scholar 

  5. Pantelides C P, Garfield T T, Richins W D, Larson T K, Blakeley J E. Reinforced concrete and fiber reinforced concrete panels subjected to blast detonations and post-blast static tests. Engineering Structures, 2014, 76: 24–33

    Article  Google Scholar 

  6. Wang H, Belarbi A. Ductility characteristics of fiber-reinforced-concrete beams reinforced with FRP rebars. Construction & Building Materials, 2011, 25(5): 2391–2401

    Article  Google Scholar 

  7. Brewka G. Artificial intelligence—a modern approach by Stuart Russell and Peter Norvig, Prentice Hall. Series in Artificial Intelligence, Englewood Cliffs, NJ. Knowledge Engineering Review, 1996, 11(1): 78

    Google Scholar 

  8. Lu P, Chen S, Zheng Y. Artificial intelligence in civil engineering. Mathematical Problems in Engineering, 2012, 2012: 1–22

    Google Scholar 

  9. Rzevski G. Artificial intelligence in engineering: past, present and future. Artificial Intelligence in Engineering, 1995, X(July): 1–14

    Google Scholar 

  10. Brunette E S, Flemmer R C, Flemmer C L. A review of artificial intelligence. ICARA 2009-Proceedings of the 4th International Conference on Autonomous Robots and Agents, 2009, 385–392

    Google Scholar 

  11. Pao Y H. Engineering artificial intelligence. Engineering Applications of Artificial Intelligence, 1988, 1(1): 5–10

    Article  Google Scholar 

  12. El-Sayed A, El-Salakawy E, Benmokrane B. Shear strength of oneway concrete slabs reinforced with fiber-reinforced polymer composite bars. Journal of Composites for Construction, 2005, 9 (2): 147–157

    Article  Google Scholar 

  13. Ince R. Prediction of fracture parameters of concrete by artificial neural networks. Engineering Fracture Mechanics, 2004, 71(15): 2143–2159

    Article  Google Scholar 

  14. Oreta A W C, Kawashima K. Neural network modeling of confined compressive strength and strain of circular concrete columns. Journal of Structural Engineering, 2003, 129(4): 554–561

    Article  Google Scholar 

  15. Khademi F, Akbari M, Jamal S M, Nikoo M. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering, 2017, 11(1): 90–99

    Article  Google Scholar 

  16. Vu-Bac N, Duong T X, Lahmer T, Zhuang X, Sauer R A, Park H S, Rabczuk T. A NURBS-based inverse analysis for reconstruction of nonlinear deformations of thin shell structures. Computer Methods in Applied Mechanics and Engineering, 2018, 331: 427–455

    Article  MathSciNet  Google Scholar 

  17. González-Fonteboa B, Martínez-Abella F. Shear strength of recycled concrete beams. Construction & Building Materials, 2007, 21(4): 887–893

    Article  Google Scholar 

  18. Kahn L F, Mitchell A D. Shear friction tests with high-strength concrete. ACI Structural Journal, 2002, 99(1): 98–103

    Google Scholar 

  19. Yaseen ZM, El-Shafie A, Afan H A, Hameed M, MohtarWHMW, Hussain A. RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Computing & Applications, 2015, doi: 10.1007/s00521-015-1952-6

    Google Scholar 

  20. Lee J, Almond D, Harris B. The use of neural networks for the prediction of fatigue lives of composite materials. Composites. Part A, Applied Science and Manufacturing, 1999, 30(10): 1159–1169

    Article  Google Scholar 

  21. Premalatha K, Natarajan A M. Hybrid PSO and GA for global maximization. Int J Open Problems Compt Math, 2009, 2(4): 597–608

    MathSciNet  Google Scholar 

  22. Hoballah A, Erlich I. PSO-ANN approach for transient stability constrained economic power generation. 2009 IEEE Bucharest PowerTech: Innovative Ideas Toward the Electrical Grid of the Future, 2009

    Google Scholar 

  23. Zhao H S, Jin L, Huang X Y. A prediction of the monthly precipitation model based on PSO-ANN and its applications. 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice, 2010, 476–479

    Google Scholar 

  24. Yi D, Ge X. An improved PSO-based ANN with simulated annealing technique. Neurocomputing, 2005, 63: 527–533

    Article  Google Scholar 

  25. Abdullah A G, Suranegara G M, Hakim D L. Hybrid PSO-ANN application for improved accuracy of short term Load Forecasting. WSEAS Transactions on Power Systems, 2014, 9: 446–451

    Google Scholar 

  26. Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H. Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Engineering with Computers, 2016, 32(4): 705–715

    Article  Google Scholar 

  27. Rukhaiyar S, Alam M N, Samadhiya N K. A PSO-ANN hybrid model for predicting factor of safety of slope. International Journal of Geotechnical Engineering, 2017, doi: 10.1080/19386362.2017.1305652

    Google Scholar 

  28. Li J, Wang J. Research of steel plate temperature prediction based on the improved PSO-ANN algorithm for roller hearth normalizing furnace. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2010, 2464–2469

    Google Scholar 

  29. Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19–31

    Article  Google Scholar 

  30. Li V C, Ward R, Hamza A M. Steel and synthetic fibers as shear reinforcement. ACI Materials Journal, 1992, 89(5): 499–508

    Google Scholar 

  31. Mansur M A, Ong K C G, Paramasivam P. Shear strength of fibrous concrete beams without stirrups. Journal of Structural Engineering, 1986, 112(9): 2066–2079

    Article  Google Scholar 

  32. Tan K H, Murugappan K, Paramasivam P. Shear behavior of steel fiber reinforced concrete beams. ACI Structural Journal, 1993, 90 (1): 155–160

    Google Scholar 

  33. Narayanan R, Darwish I Y S. Use of steel fibers as shear reinforcement. ACI Structural Journal, 1987, 84(3): 216–227

    Google Scholar 

  34. Ashour S A, Hasanain G S, Wafa F F. Shear behavior of high-strength fiber reinforced concrete beams. ACI Structural Journal, 1992, 89(2): 176–184

    Google Scholar 

  35. Swamy R N, Jones R, Chiam A T P. Influence of steel fibers on the shear resistance of lightweight concrete I-beams. ACI Structural Journal, 1993, 90(1): 103–114

    Google Scholar 

  36. Narayanan R, Darwish I Y S. Fiber concrete deep beams in shear. ACI Structural Journal, 1988, 85(2): 141–149

    Google Scholar 

  37. Sanad A, Saka M P. Prediction of ultimate shear strength of reinforced concrete deep beams using neuronal networks. Journal of Structural Engineering, 2001, 127(7): 818–828

    Article  Google Scholar 

  38. Adebar P, Mindess S, St.-Pierre D, Olund B. Shear tests of fiber concrete beams without stirrups. ACI Structural Journal, 1997, 94 (1): 68–76

    Google Scholar 

  39. Vu-Bac N, Lahmer T, Keitel H, Zhao J, Zhuang X, Rabczuk T. Stochastic predictions of bulk properties of amorphous polyethylene based on molecular dynamics simulations. Mechanics of Materials, 2014a, 68: 70–84

    Article  Google Scholar 

  40. Vu-Bac N, Lahmer T, Zhang Y, Zhuang X, Rabczuk T. Stochastic predictions of interfacial characteristic of polymeric nanocomposites (PNCs). Composites Part B: Engineering, 2014b, 59: 80–95

    Article  Google Scholar 

  41. Vu-Bac N, Silani M, Lahmer T, Zhuang X, Rabczuk T. A unified framework for stochastic predictions of mechanical properties of polymeric nanocomposites. Computational Materials Science, 2015a, 96: 520–535

    Article  Google Scholar 

  42. Vu-Bac N, Rafiee R, Zhuang X, Lahmer T, Rabczuk T. Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters. Composites. Part B, Engineering, 2015b, 68: 446–464

    Article  Google Scholar 

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  1. Projects and Reconstruction Department, University of Baghdad, Baghdad, Iraq

    Abeer A. Al-Musawi

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  1. Abeer A. Al-Musawi
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Al-Musawi, A.A. Determination of shear strength of steel fiber RC beams: application of data-intelligence models. Front. Struct. Civ. Eng. 13, 667–673 (2019). https://doi.org/10.1007/s11709-018-0504-4

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  • DOI: https://doi.org/10.1007/s11709-018-0504-4

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