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A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

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Abstract

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

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References

  1. Mafipour MS, Tatlari S, Ghiami Azad AR, Shahverdi M, Mohammadi S (2019) Fatigue behavior of headed stud shear connectors in steel-concrete composite bridge girders. In: 3rd International conference on applied research in structural engineering and constructional management, 25–26 June 2019, Tehran, Iran

  2. Ghiami Azad AR, MS Mafipour, S Tatlari (2018) Fatigue behavior of shear connectors in steel-concrete beams with partial interaction. In: 3rd International conference on steel & structure, Tehran, Iran, 11–12 December 2018

  3. Mafipour MS, Homayoun FA, Tatlari S, Ghiami Azad AR (2019) Closed-form formulations in composite beams based on partially-composite behavior. In: 3rd International conference on applied research in structural engineering and constructional management, Tehran, Iran, 25–26 June 2019

  4. Ghiami Azad AR, Mafipour MS, Tatlari S (2018) A novel method for linear analysis of partially-composite beams. In: 3rd International conference on steel & structure, Tehran, Iran, 11–12 December 2018

  5. Wei X, Shariati M, Zandi Y, Pei S, Jin Z, Gharachurlu S, Abdullahi M, Tahir M, Khorami M (2018) Distribution of shear force in perforated shear connectors. Steel Compos Struct 27(3):389–399

    Google Scholar 

  6. Tahmasbi F, Maleki S, Shariati M, Ramli Sulong NH, Tahir MM (2016) shear capacity of C-shaped and L-shaped angle shear connectors. PLoS ONE 11(8):e0156989

    Google Scholar 

  7. Khorramian K, Maleki S, Shariati M, Ramli Sulong NH (2015) Behavior of tilted angle shear connectors. PLoS ONE 10(12):0144288

    Google Scholar 

  8. Shariati M, Ramli Sulong NH, Suhatril M, Shariati A, Arabnejad MMK, Sinaei H (2012) Behaviour of C-shaped angle shear connectors under monotonic and fully reversed cyclic loading: an experimental study. Mater Des 41:67–73

    Google Scholar 

  9. Shariati M, Toghroli A, Jalali A, Ibrahim Z (2017) Assessment of stiffened angle shear connector under monotonic and fully reversed cyclic loading. In: 5th International conference on advances in civil, structural and mechanical engineering—CSM 2017, Zurich, Switzerland

  10. Paknahad M, Shariati M, Sedghi Y, Bazzaz M, Khorami M (2018) Shear capacity equation for channel shear connectors in steel-concrete composite beams. Steel Compos Struct 28(4):483–494

    Google Scholar 

  11. Shariati Mahdi, Ramli Sulong NH, Suhatril Meldi, Ali Shariati MM, Khanouki Arabnejad, Sinaei Hamid (2013) Comparison of behaviour between channel and angle shear connectors under monotonic and fully reversed cyclic loading. Constr Build Mater 38:582–593

    Google Scholar 

  12. Rao SN (1970) Composite construction-tests on small scale shear connectors. The Institute of Engineers, Australia. Civil Engineering Transactions, April

  13. Hiroshi Y, Kiyomia O (1987) Load carrying capacity of shear connectors made of shape steel in steel-concrete composite members. Structures division subaqueous tunnels and pipelines laboratory, 1987. PARI Techinical Note 0595

  14. Ciutina AL, Stratan A (2008) Cyclic performances of shear connectors. ASCE

  15. Choi SM, Tateishi K, Uchida D, Asano K, Kobayashi K (2008) Fatigue strength of angle shape shear connector used in steel-concrete composite slab. Int J Steel Struct 8(3):199–204

    Google Scholar 

  16. Choi SM (2011) Fatigue resistance of angle shape shear connector used in steel-concrete composite slab. 2011, Ph.D. thesis, Graduate School of Engineering of Nagoya University, Japan

  17. Fukazawa K, Sakai M, Sudou N, Kobayashi K (2002) Fatigue durability of steel-concrete composite slab, MELAB and application to continuous composite steel girder bridge. Mitsui Zosen Tech Rev 6:8–18

    Google Scholar 

  18. Saidi T, Furuuchi H, Ueda T (2008) The transferred shear force-relative displacement relationship of the shear connector in steel-concrete sandwich beam and its model. Doboku Gakkai Ronbunshuu E 64(1):122–141

    Google Scholar 

  19. Ros S, Shima H (2009) A new beam type test method for load-slip relationship of L-shape shear connector. In: The 8th symposium on research and application of hybrid and composite structures, vol 18, pp 1–8

  20. Khalilian M (2013) The assessment of shear capacity of angle shear connectors. Sharif University of Technology

  21. Shariati M, Shariati A, Sulong NR, Suhatril M, Khanouki MA (2014) Fatigue energy dissipation and failure analysis of angle shear connectors embedded in high strength concrete. Eng Fail Anal 41:124–134

    Google Scholar 

  22. Shariati Mahdi, Ramli Sulong NH, Arabnejad Khanouki MM, Mehrdad M (2011) Shear resistance of channel shear connectors in plain, reinforced and lightweight concrete. Scientific Research and Essays. 6(4):977–983

    Google Scholar 

  23. Shariati M, Ramli Sulong NH, Sinaei H, Khanouki A, Mehdi M, Shafigh P (2011) Behavior of channel shear connectors in normal and light weight aggregate concrete (experimental and analytical study). Adv Mater Res 168:2303–2307

    Google Scholar 

  24. Shariati M, Ramli Sulong NH, Arabnejad Khanouki MM, Shariati A (2011) Experimental and numerical investigations of channel shear connectors in high strength concrete. In: Proceedings of the 2011 world congress on advances in structural engineering and mechanics (ASEM’11+), Seoul, South Korea

  25. Shariati M, Ramli Sulong N, Suhatril M, Shariati A, Arabnejad Khanouki M, Sinaei H (2012) Fatigue energy dissipation and failure analysis of channel shear connector embedded in the lightweight aggregate concrete in composite bridge girders. In 5th International conference on engineering failure analysis 1–4 July 2012, Hilton Hotel, The Hague, The Netherlands

  26. Shariati A, Sulong NR, Suhatril M, Shariati M (2012) Investigation of channel shear connectors for composite concrete and steel T-beam. Int J Phys Sci 7(11):1828–1831

    Google Scholar 

  27. Nasrollahi S, Maleki S, Shariati M, Marto A, Khorami M (2018) Investigation of pipe shear connectors using push out test. Steel Compos Struct 27(5):537–543

    Google Scholar 

  28. Shariati M, Tahir MM, Wee TC, Shah SN, Jalali A, Abdullahi MA, Khorami M (2018) Experimental investigations on monotonic and cyclic behavior of steel pallet rack connections. Eng Fail Anal 85:149–166

    Google Scholar 

  29. Shariati M, Sulong NR, Shariati A, Khanouki MA (2015) Behavior of V-shaped angle shear connectors: experimental and parametric study. Mater Struct 49(9):3909–3926

    Google Scholar 

  30. Shahabi S, Sulong N, Shariati M, Shah S (2016) Performance of shear connectors at elevated temperatures-A review. Steel Compos Struct 20(1):185–203

    Google Scholar 

  31. Toghroli A, Mohammadhassani M, Suhatril M, Shariati M, Ibrahim Z (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct 17(5):623–639

    Google Scholar 

  32. Sedghi Y, Zandi Y, Toghroli A, Safa M, Mohamad ET, Khorami M, Wakil K (2018) Application of ANFIS technique on performance of C and L shaped angle shear connectors. Smart Struct Syst 22(3):335–340

    Google Scholar 

  33. Trung NT, Shahgoli AF, Zandi Y, Shariati M, Wakil K, Safa M, Khorami M (2019) Moment-rotation prediction of precast beam-to-column connections using extreme learning machine. Struct Eng Mech 70(5):639–647

    Google Scholar 

  34. Shariati M, Trung NT, Wakil K, Mehrabi P, Safa M, Khorami M (2019) Moment-rotation estimation of steel rack connection using extreme learning machine. Steel Compos Struct 31(5):427–435

    Google Scholar 

  35. Gao W, Karbasi M, Derakhsh AM, Jalili A (2018) Development of a novel soft-computing framework for the simulation aims: a case study. Eng Comput 35(1):1–8

    Google Scholar 

  36. Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507

    Google Scholar 

  37. Katebi J, Shoaei-parchin M, Shariati M, Trung NT, Khorami M (2019) Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures. Eng Comput 1–20

  38. Ma CK, Lee YH, Awang AZ, Omar W, Mohammad S, Liang M (2019) Artificial neural network models for FRP-repaired concrete subjected to pre-damaged effects. Neural Comput Appl 31(3):711–717

    Google Scholar 

  39. Shariati M, Mafipour MS, Mehrabi P, Bahadori A, Zandi Y, Salih MN, Nguyen H, Dou J, Song X, Poi-Ngian S (2019) Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete. Appl Sci 9(24):5534

    Google Scholar 

  40. Shariati M, Mafipour MS, Mehrabi P, Zandi Y, Dehghani D, Bahadori A, Shariati A, Trung NT, Salih MN, Poi-Ngian S (2019) Application of extreme learning machine (ELM) and genetic programming (GP) to design steel-concrete composite floor systems at elevated temperatures. Steel Compos Struct 33(3):319

    Google Scholar 

  41. Khademi F, Akbari M, Jamal SM, Nikoo M (2017) Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civil Eng 11(1):90–99

    Google Scholar 

  42. Toghroli A, Suhatril M, Ibrahim Z, Safa M, Shariati M, Shamshirband S (2018) Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. J Intell Manuf 29(8):1793–1801

    Google Scholar 

  43. Naderpour H, Mirrashid M (2019) Moment capacity estimation of spirally reinforced concrete columns using ANFIS. Complex Intell Syst 5(2):1–11

    Google Scholar 

  44. Basarir H, Elchalakani M, Karrech A (2019) The prediction of ultimate pure bending moment of concrete-filled steel tubes by adaptive neuro-fuzzy inference system (ANFIS). Neural Comput Appl 31(2):1239–1252

    Google Scholar 

  45. ASTM C150 (2012) Standard specification of portland cement. ASTM International West Conshohocken, PA

  46. ASTM C39 (2005) Standard test method for compressive strength of cylindrical concrete specimens. In: Annual book of ASTM standards

  47. Shariati A, Shariati M, Sulong NR, Suhatril M, Khanouki MA, Mahoutian M (2014) Experimental assessment of angle shear connectors under monotonic and fully reversed cyclic loading in high strength concrete. Constr Build Mater 52:276–283

    Google Scholar 

  48. Shariati M, Sulong NR, Khanouki MA (2012) Experimental assessment of channel shear connectors under monotonic and fully reversed cyclic loading in high strength concrete. Mater Des 34:325–331

    Google Scholar 

  49. Davoodnabi SM, Mirhosseini SM, Shariati M (2019) Behavior of steel-concrete composite beam using angle shear connectors at fire condition. Steel Compos Struct 30(2):141–147

    Google Scholar 

  50. Shariati M, Sulong NR, Shariati A, Kueh AB (2016) Comparative performance of channel and angle shear connectors in high strength concrete composites: an experimental study. Constr Build Mater 120:382–392

    Google Scholar 

  51. Priddy KL, Keller PE (2005) Artificial neural networks: an introduction, vol 68. SPIE press, Bellingham

    Google Scholar 

  52. Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32(4):631–644

    Google Scholar 

  53. Jain AK, Mao J, Mohiuddin KM (1996) Artificial neural networks: a tutorial. Computer 29(3):31–44

    Google Scholar 

  54. Moosazadeh S, Namazi E, Aghababaei H, Marto A, Mohamad H, Hajihassani M (2018) Prediction of building damage induced by tunnelling through an optimized artificial neural network. Eng Comput 35(2):1–13

    Google Scholar 

  55. Prasad BR, Eskandari H, Reddy BV (2009) Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Constr Build Mater 23(1):117–128

    Google Scholar 

  56. Karlik B, Olgac AV (2011) Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst 1(4):111–122

    Google Scholar 

  57. Li J, Cheng J-H, Shi J-Y, Huang F (2012) Brief introduction of back propagation (BP) neural network algorithm and its improvement. In: Advances in computer science and information engineering, 2012, Springer. pp 553–558

  58. Mansouri I, Shariati M, Safa M, Ibrahim Z, Tahir M, Petković D (2017) Analysis of influential factors for predicting the shear strength of a V-shaped angle shear connector in composite beams using an adaptive neuro-fuzzy technique. J Intell Manuf 30(3):1–11

    Google Scholar 

  59. Safa M, Shariati M, Ibrahim Z, Toghroli A, Baharom SB, Nor NM, Petkovic D (2016) Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam’s shear strength. Steel Compos Struct 21(3):679–688

    Google Scholar 

  60. Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Google Scholar 

  61. Petković D, Ćojbašić Ž, Nikolić V, Shamshirband S, Kiah MLM, Anuar NB, Wahab AWA (2014) Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission. Energy 64:868–874

    Google Scholar 

  62. Petković D, Issa M, Pavlović ND, Pavlović NT, Zentner L (2012) Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst Appl 39(10):9477–9482

    Google Scholar 

  63. Mayilvaganan MK, Naidu K (2011) Comparison of membership functions in adaptive-network-based fuzzy inference system (ANFIS) for the prediction of groundwater level of a watershed. J Computer Appl Res Dev 1:35–42

    Google Scholar 

  64. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1):489–501

    Google Scholar 

  65. Huang G-B (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Networks 14(2):274–281

    Google Scholar 

  66. Al-Shamiri AK, Kim JH, Yuan T-F, Yoon YS (2019) Modeling the compressive strength of high-strength concrete: an extreme learning approach. Constr Build Mater 208:204–219

    Google Scholar 

  67. Armaghani DJ, Hasanipanah M, Amnieh HB, Bui DT, Mehrabi P, Khorami M (2019) Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm. Engineering with Computers 3:1–13

    Google Scholar 

  68. Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241

    Google Scholar 

  69. Willmott CJ (1981) On the validation of models. Phys Geogr 2(2):184–194

    Google Scholar 

  70. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10(3):282–290

    Google Scholar 

  71. Mao KZ (2004) Orthogonal forward selection and backward elimination algorithms for feature subset selection. IEEE Trans Syst Man Cybern Part B (Cybern) 34(1):629–634

    Google Scholar 

  72. Chen J, Chen Z (2008) Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95(3):759–771

    MathSciNet  MATH  Google Scholar 

  73. Sakamoto Y, Ishiguro M, Kitagawa G (1986) Akaike information criterion statistics. D. Reidel, Dordrecht, p 81

    MATH  Google Scholar 

  74. Mallows CL (1973) Some comments on C p. Technometrics 15(4):661–675

    MATH  Google Scholar 

  75. Shariati M., Mafipour MS, Haido JH, Yousif ST, Toghroli A, Trung NT, Shariati A (2020) Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Steel Compos Struct 34(1):155–170

  76. Lourakis MI (2005) A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Foundation of Research and Technology 4(1):1–6

    Google Scholar 

  77. Moré JJ (1978) The Levenberg–Marquardt algorithm: implementation and theory. In: Numerical analysis, Springer. pp 105–116

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam

    Mahdi Shariati & Ali Toghroli

  2. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mohammad Saeed Mafipour

  3. Department of Civil Engineering, K.N Toosi University of Technology, Tehran, Iran

    Peyman Mehrabi

  4. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

    Ali Shariati & Nguyen Thoi Trung

  5. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

    Ali Shariati & Nguyen Thoi Trung

  6. School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Musab N. A. Salih

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  1. Mahdi Shariati
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Correspondence to Ali Shariati.

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Shariati, M., Mafipour, M.S., Mehrabi, P. et al. A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques. Engineering with Computers 37, 2089–2109 (2021). https://doi.org/10.1007/s00366-019-00930-x

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