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Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

  • Safa, M. (Department of Civil Engineering, University of Malaya) ;
  • Shariati, M. (Department of Civil Engineering, University of Malaya) ;
  • Ibrahim, Z. (Department of Civil Engineering, University of Malaya) ;
  • Toghroli, A. (Department of Civil Engineering, University of Malaya) ;
  • Baharom, Shahrizan Bin (Department of Civil and Structural Engineering, National University of Malaysia) ;
  • Nor, Norazman M. (National Defence University of Malaysia) ;
  • Petkovic, Dalibor (University of Nis, Faculty of Mechanical Engineering, Department of Mechatronics and Control)
  • Received : 2015.12.21
  • Accepted : 2016.05.21
  • Published : 2016.06.30

Abstract

Structural design of a composite beam is influenced by two main factors, strength and ductility. For the design to be effective for a composite beam, say an RC slab and a steel I beam, the shear strength of the composite beam and ductility have to carefully estimate with the help of displacements between the two members. In this investigation the shear strengths of steel-concrete composite beams was analyzed based on the respective variable parameters. The methodology used by ANFIS (Adaptive Neuro Fuzzy Inference System) has been adopted for this purpose. The detection of the predominant factors affecting the shear strength steel-concrete composite beam was achieved by use of ANFIS process for variable selection. The results show that concrete compression strength has the highest influence on the shear strength capacity of composite beam.

Keywords

Acknowledgement

Supported by : University of Malaya

References

  1. Bailey, C., Burgess, I., Plank, R., El-Rimawi, J. and Huang, Z., Bailey, C.G., Lennon, T. and Moore, D.B. (1999), "The behaviour of full-scale steel-framed buildings subjected to compartment fires", The Struct. Eng., 77(8), 15-21.
  2. Dong, Y. and Prasad, K. (2009), "Experimental study on the behavior of full-scale composite steel frames under furnace loading", J. Struct. Eng., 135(10), 1278-1289. https://doi.org/10.1061/(ASCE)0733-9445(2009)135:10(1278)
  3. Duan, S., Wang, J., Zhou, Q. and Wang, H. (2010), "An experimental study on double steel-concrete composite beam specimens", In: Challenges, Opportunities and Solutions in Structural Engineering and Construction, Taylor & Francis Group, London, UK, pp. 209-214.
  4. Ekici, B.B. and Aksoy, U.T. (2011), "Prediction of building energy needs in early stage of design by using ANFIS", Expert Syst. Appl., 38(5), 5352-5358. https://doi.org/10.1016/j.eswa.2010.10.021
  5. Hakim, S.J.S., Noorzaei, J., Jaafar, M., Jameel, M. and Mohammadhassani, M. (2011), "Application of artificial neural networks to predict compressive strength of high strength concrete", Int. J. Phys. Sci., 6(5), 975-981.
  6. Inal, M. (2008), "Determination of dielectric properties of insulator materials by means of ANFIS: a comparative study", J. Mater. Process. Technol., 195(1), 34-43. https://doi.org/10.1016/j.jmatprotec.2007.04.106
  7. Jang, J.-S. (1993), "ANFIS: Adaptive-network-based fuzzy inference system", IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541
  8. Khajeh, A., Modarress, H. and Rezaee, B. (2009), "Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers", Expert Syst. Appl., 36(3), 5728-5732. https://doi.org/10.1016/j.eswa.2008.06.051
  9. Kurnaz, S., Cetin, O. and Kaynak, O. (2010), "Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles", Expert Syst. Appl., 37(2), 1229-1234. https://doi.org/10.1016/j.eswa.2009.06.009
  10. Liew, R.J.Y. and Xiong, D.X. (2009), "Effect of preload on the axial capacity of concrete-filled composite columns", J. Construct. Steel Res., 65(3), 709-722. https://doi.org/10.1016/j.jcsr.2008.03.023
  11. Lo, S.-P. and Lin, Y.-Y. (2005), "The prediction of wafer surface non-uniformity using FEM and ANFIS in the chemical mechanical polishing process", Journal of Materials Processing Technology, 168(2), 250-257. https://doi.org/10.1016/j.jmatprotec.2005.01.010
  12. Maleki, S. and Bagheri, S. (2008), "Behavior of channel shear connectors, Part I: Experimental study", J. Construct. Steel Res., 64(12), 1333-1340. https://doi.org/10.1016/j.jcsr.2008.01.010
  13. Maleki, S. and Mahoutian, M. (2009), "Experimental and analytical study on channel shear connectors in fiber-reinforced concrete", J. Construct. Steel Res., 65(8-9), 1787-1793. https://doi.org/10.1016/j.jcsr.2009.04.008
  14. Michels, J., Martinelli, E., Czaderski, C. and Motavalli, M. (2014), "Prestressed CFRP strips with gradient anchorage for structural concrete retrofitting: Experiments and numerical modeling", Polymers, 6(1), 114-131. https://doi.org/10.3390/polym6010114
  15. Mohammadhassani, M., Nezamabadi-Pour, H., Jameel, M. and Garmasiri, K. (2013), "Applications of the ANFIS and LR in the prediction of strain in tie section of concrete deep beams", Comput. Concrete, 12(3), 243-259. https://doi.org/10.12989/cac.2013.12.3.243
  16. Mohammadhassani, M., Nezamabadi-Pour, J., Suhatril, M. and Shariati, M. (2013), "Identification of a suitable ANN architecture in predicting strain in tie section of concrete deep beams", Struct. Eng. Mech., Int. J., 46(6), 853-868. https://doi.org/10.12989/sem.2013.46.6.853
  17. Mohammadhassani, M., Nezamabadi-Pour, M., Suhatril, M. and Shariati, M. (2014a), "An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups", Smart Struct. Syst., Int. J., 14(5), 785-809. https://doi.org/10.12989/sss.2014.14.5.785
  18. Mohammadhassani, M., Suhatril, M., Shariati, M. and Ghanbari, F. (2014b), "Ductility and strength assessment of HSC beams with varying of tensile reinforcement ratios", Struct. Eng. Mech., Int. J., 48(6), 833-848.
  19. Petkovic, D., Issa, M., Pavlovic, N.D., Zentner, L. and Cojbasic, Z. (2012), "Adaptive neuro fuzzy controller for adaptive compliant robotic gripper", Expert Syst. Appl., 39(18), 13295-13304. https://doi.org/10.1016/j.eswa.2012.05.072
  20. Prakash, A., Anandavalli, N., Madheswaran, C. and Lakshmanan, N. (2012), "Modified push-out tests for determining shear strength and stiffness of HSS stud connector-experimental study", Int. J. Compos. Mater., 2(3), 22-31. https://doi.org/10.5923/j.cmaterials.20120203.02
  21. Queiroz, F., Vellasco, P. and Nethercot, D. (2007), "Finite element modelling of composite beams with full and partial shear connection", J. Construct. Steel Res., 63(4), 505-521. https://doi.org/10.1016/j.jcsr.2006.06.003
  22. Shariati, M., Ramli Sulong, N.H. and Arabnejad Khanouki, M.M. (2010), "Experimental and analytical study on channel shear connectors in light weight aggregate concrete", Proceedings of the 4th International Conference on Steel & Composite Structures, Sydney, Australia, July.
  23. Shariati, M., Ramli Sulong, N.H., Arabnejad Khanouki, M.M. and Shariati, A. (2011a), "Experimental and numerical investigations of channel shear connectors in high strength concrete", Proceedings of the 2011 World Congress on Advances in Structural Engineering and Mechanics (ASEM'11+), Seoul, South Korea, September.
  24. Shariati, M., Ramli Sulong, N.H., Arabnejad Khanouki, M.M. and Mahoutian, M. (2011b), "Shear resistance of channel shear connectors in plain, reinforced and lightweight concrete", Sci. Res. Essays, 6(4), 977-983.
  25. Shariati, M., Ramli Sulong, N.H., Sinaei, H., Arabnejad Khanouki, M.M. and Shafigh, P. (2011c), "Behavior of channel shear connectors in normal and light weight aggregate concrete (experimental and analytical study)", Adv. Mater. Res., 168, 2303-2307.
  26. Shariati, M., Ramli Sulong, N.H., Arabnejad Khanouki, M.M., Shafigh, P. and Sinaei, H. (2011d), "Assessing the strength of reinforced concrete structures through Ultrasonic Pulse Velocity and Schmidt Rebound Hammer tests", Sci. Res. Essays, 6(1), 213-220.
  27. Shariati, M., Ramli Sulong, N.H. and Arabnejad Khanouki, M.M. (2012a), "Experimental assessment of channel shear connectors under monotonic and fully reversed cyclic loading in high strength concrete", Mater. Des., 34, 325-331. https://doi.org/10.1016/j.matdes.2011.08.008
  28. Shariati, A., Ramli Sulong, N.H., Suhatril, M. and Shariati, M. (2012b), "Investigation of channel shear connectors for composite concrete and steel T-beam", Int. J. Phys. Sci., 7(11), 1828-1831.
  29. Shariati, A., Ramli Sulong, N.H., Suhatril, M. and Shariati, M. (2012c), "Various types of shear connectors in composite structures: A review", Int. J. Phys. Sci., 7(22), 2876-2890.
  30. Shariati, M., Ramli Sulong, N.H., Suhatril, M., Shariati, A., Arabnejad Khanouki, M. and Sinaei, H. (2012d), "Fatigue energy dissipation and failure analysis of channel shear connector embedded in the lightweight aggregate concrete in composite bridge girders", Proceedings of the 5th International Conference on Engineering Failure Analysis, The Hague, The Netherlands, July.
  31. Shariati, M., Ramli Sulong, N.H., Suhatril, M., Shariati, A., Khanouki, M.M.A. and Sinaei, H. (2012e), "Behaviour of C-shaped angle shear connectors under monotonic and fully reversed cyclic loading: An experimental study", Mater. Des., 41, 67-73. https://doi.org/10.1016/j.matdes.2012.04.039
  32. Shariati, M., Ramli Sulong, N.H., Suhatril, M., Shariati, A., Arabnejad Khanouki, M.M. and Sinaei, H. (2013), "Comparison of behaviour between channel and angle shear connectors under monotonic and fully reversed cyclic loading", Construct. Build. Mater., 38, 582-593. https://doi.org/10.1016/j.conbuildmat.2012.07.050
  33. Shariati, A., Shariati, M., Ramli Sulong, N.H., Suhatril, M., Arabnejad Khanouki, M.M. and Mahoutian, M. (2014), "Experimental assessment of angle shear connectors under monotonic and fully reversed cyclic loading in high strength concrete", Construct. Build. Mater., 52, 276-283. https://doi.org/10.1016/j.conbuildmat.2013.11.036
  34. Slutter, R.G. (1963), "Pushout tests of welded stud shear connectors in lightweight concrete", Report No. 200.63.409.1; Fritz Engineering Laboratory, Lehigh University, Bethlehem, PA, USA.
  35. Tian, L. and Collins, C. (2005), "Adaptive neuro-fuzzy control of a flexible manipulator", Mechatronics, 15(10), 1305-1320. https://doi.org/10.1016/j.mechatronics.2005.02.001
  36. Toghroli, A., Mohammadhassani, M., Shariati, M., Suhatril, M., Ibrahim, Z. and Ramli Sulong, N.H. (2014), "Prediction of shear capacity of channel shear connectors using the ANFIS model", Steel Compos. Struct., Int. J., 17(5), 623-639. https://doi.org/10.12989/scs.2014.17.5.623
  37. Viest, I.M., Siessr, C.P., Appleton, J.H. and Newmark, N.M. (1952), "Full scale test of channel shear connectors and composite T-beams", Bulletin Series No. 405.
  38. Wang, A.J. and Chung, K.F. (2008), "Advanced finite element modelling of perforated composite beams with flexible shear connectors", Eng. Struct., 30(10), 2724-2738. https://doi.org/10.1016/j.engstruct.2008.03.001

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  89. In-plane varying bending force effects on wave dispersion characteristics of single-layered graphene sheets vol.10, pp.2, 2021, https://doi.org/10.12989/anr.2021.10.2.101
  90. Ionic liquid coated magnetic core/shell CoFe2O4@SiO2 nanoparticles for the separation/analysis of trace gold in water sample vol.10, pp.3, 2016, https://doi.org/10.12989/anr.2021.10.3.295
  91. Optimization algorithms for composite beam as smart active control of structures using genetic algorithms vol.27, pp.6, 2016, https://doi.org/10.12989/sss.2021.27.6.1041
  92. Assessment of microstructure and surface effects on vibrational characteristics of public transportation vol.11, pp.1, 2021, https://doi.org/10.12989/anr.2021.11.1.101
  93. Smart estimation of automatic approach in enhancing the road safety under AASHTO Standard specification and STM vol.79, pp.3, 2021, https://doi.org/10.12989/sem.2021.79.3.389
  94. Application of multi-hybrid metaheuristic algorithm on prediction of split-tensile strength of shear connectors vol.28, pp.2, 2016, https://doi.org/10.12989/sss.2021.28.2.167
  95. Development of a Hybrid Artificial Neural Network-Particle Swarm Optimization Model for the Modelling of Traffic Flow of Vehicles at Signalized Road Intersections vol.11, pp.18, 2016, https://doi.org/10.3390/app11188387
  96. Analyzing shear strength of steel-concrete composite beam with angle connectors at elevated temperature using finite element method vol.40, pp.6, 2016, https://doi.org/10.12989/scs.2021.40.6.853
  97. Analyzing the energy performance of buildings by neuro-fuzzy logic based on different factors vol.23, pp.12, 2016, https://doi.org/10.1007/s10668-021-01382-4
  98. Evaluation and monitoring of impact resistance of fiber reinforced concrete by adaptive neuro fuzzy algorithm vol.34, pp.None, 2016, https://doi.org/10.1016/j.istruc.2021.09.072
  99. Adaptive neuro fuzzy evaluation of energy and non‐energy material productivity impact on sustainable development based on circular economy and gross domestic product vol.31, pp.1, 2016, https://doi.org/10.1002/bse.2878
  100. Application of ANFIS in the preparation of expert opinions and evaluation of building design variants in the context of processing large amounts of data vol.133, pp.None, 2016, https://doi.org/10.1016/j.autcon.2021.104045
  101. Engine performance fueled with jojoba biodiesel and enzymatic saccharification on the yield of glucose of microbial lipids biodiesel vol.239, pp.no.pd, 2016, https://doi.org/10.1016/j.energy.2021.122390
  102. Experimental and numerical studies on novel stiffener-enhanced steel-concrete-steel sandwich panels subjected to impact loading vol.45, pp.None, 2022, https://doi.org/10.1016/j.jobe.2021.103479
  103. Numerical and theoretical studies on shear behavior of steel-UHPC composite beams with waffle slab vol.47, pp.None, 2022, https://doi.org/10.1016/j.jobe.2021.103913