Structural Engineering and Mechanics

  • 제61권2호
  • /
  • Pages.283-293
  • /
  • 2017
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Intelligent fuzzy inference system approach for modeling of debonding strength in FRP retrofitted masonry elements

  • 투고 : 2016.07.17
  • 심사 : 2016.11.17
  • 발행 : 2017.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

The main contribution of the present paper is to propose an intelligent fuzzy inference system approach for modeling the debonding strength of masonry elements retrofitted with Fiber Reinforced Polymer (FRP). To achieve this, the hybrid of meta-heuristic optimization methods and adaptive-network-based fuzzy inference system (ANFIS) is implemented. In this study, particle swarm optimization with passive congregation (PSOPC) and real coded genetic algorithm (RCGA) are used to determine the best parameters of ANFIS from which better bond strength models in terms of modeling accuracy can be generated. To evaluate the accuracy of the proposed PSOPC-ANFIS and RCGA-ANFIS approaches, the numerical results are compared based on a database from laboratory testing results of 109 sub-assemblages. The statistical evaluation results demonstrate that PSOPC-ANFIS in comparison with ANFIS-RCGA considerably enhances the accuracy of the ANFIS approach. Furthermore, the comparison between the proposed approaches and other soft computing methods indicate that the approaches can effectively predict the debonding strength and that their modeling results outperform those based on the other methods.

키워드

참고문헌

  1. Abdollahi Chahkand, N., Zamin Jumaat, M., Ramli Sulong, N.H., Zhao, X.L. and Mohammadizadeh, M.R. (2014), "Experimental and theoretical investigation on torsional behavior of CFRP strengthened square hollow steel section", Thin. Wall. Struct., 68, 135-140.
  2. Alshihri, M.M., Azmy, A.M. and El-Bisy, M.S. (2009), "Neural networks for predicting compressive strength of structural light weight concrete", Constr. Build. Mater., 23, 2214-2219. https://doi.org/10.1016/j.conbuildmat.2008.12.003
  3. Araujo, E. and Coelho, L.D.S. (2008), "Particle swarm approaches using Lozi map chaotic sequences to fuzzy modeling of an experimental thermal-vacuum system", App. Soft. Comput., 8, 1354-1364. https://doi.org/10.1016/j.asoc.2007.10.016
  4. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Exp. Syst. Appl., 38, 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156
  5. Bal, L. and Buyle-Bodin, F. (2013), "Artificial neural network for predicting drying shrinkage of concrete", Constr. Build. Mater., 38, 248-254. https://doi.org/10.1016/j.conbuildmat.2012.08.043
  6. Bedirhanoglu, I. (2014), "A practical neuro-fuzzy model for estimating modulus of elasticity of concrete", Sruct. Eng. Mech., 51(2), 249-265. https://doi.org/10.12989/sem.2014.51.2.249
  7. Bezdek, J.C. (1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.
  8. Carrara, P. and Freddi, F. (2014), "Statistical assessment of a design formula for the debonding resistance of FRP reinforcements externally glued on masonry units", Compos. Part B. Eng., 66, 65-82.
  9. Ceroni, F., Ferracuti, B., Pecce, M. and Savoia, M. (2014), "Assessment of a bond strength model for FRP reinforcement externally bonded over masonry blocks", Compos. Part B. Eng., 61, 147-161. https://doi.org/10.1016/j.compositesb.2014.01.028
  10. Ceroni, F., Garofano, F. and Pecce. M. (2014), "Modeling of the bond behavior of tuff elements externally bonded with FRP sheets", Compos. Part B. Eng., 59, 248-259. https://doi.org/10.1016/j.compositesb.2013.12.007
  11. Chen, J.F. and Teng, J.G. (2001), "Anchorage strength models for FRP and steel plates bonded to concrete", J. Struct. Eng., ASCE, 127, 784-791. https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(784)
  12. Chitti, H., Khatibinia, M., Akbarpour, A. and Naseri, H.R. (2016), "Reliability-based design optimization of concrete gravity dams using subset simulation", Int. J. Optim. Civil. Eng., 6(3), 329-348.
  13. Chou, J.S., Chiu, C.K., Farfoura, M. and Al-Taharwa, I. (2011), "Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques", J. Comput. Civ. Eng., 25, 242-253. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000088
  14. D'Antino, T. and Pellegrino, C. (2014), "Bond between FRP composites and concrete: assessment of design procedures and analytical models", Compos. Part B. Eng., 60, 440-456. https://doi.org/10.1016/j.compositesb.2013.12.075
  15. Das Sharma, K., Chatterjee, A. and Rakshit, A. (2009), "A hybrid approach for design of stable adaptive fuzzy controllers employing Lyapunov theory and particle swarm optimization", IEEE. T. Fuzzy. Syst., 17, 329-342. https://doi.org/10.1109/TFUZZ.2008.2012033
  16. De Lorenzis, L., Paggi, M. Carpinteri, A. and Zavarise, G. (2010), "Linear elastic fracture mechanics approach to plate end debonding in rectilinear and curved plated beams", Int. J. Sol. Struct., 13, 875-889.
  17. Deb, K. (2001), Multi Objective Optimization using Evolutionary Algorithms, John Wiley & Sons.
  18. El-Zonkoly, A.M., Khalil, A.A. and Ahmied, N.M. (2009), "Optimal tuning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization", Expert. Syst. Appl., 36, 2097-2106. https://doi.org/10.1016/j.eswa.2007.12.069
  19. Ferracuti, B., Savoia, M. and Mazzotti. C. (2006), "A numerical model for FRP concrete delamination", Compos. Part B. Eng., 37, 356-364.
  20. Ferracuti, B., Savoia, M. and Mazzotti. C. (2007), "Interface law for FRP-concrete delamination", Compos. Struct., 80, 523-531. https://doi.org/10.1016/j.compstruct.2006.07.001
  21. Gharehbaghi, S. and Khatibinia, M. (2015), "Optimal seismic design of reinforced concrete structures under time history earthquake loads using an intelligent hybrid algorithm", Earthq. Eng. Eng. Vib., 14, 97-109. https://doi.org/10.1007/s11803-015-0009-2
  22. Gholizadeh, S. (2015), "Performance-based optimum seismic design of steel structures by a modified firefly algorithm and a new neural network", Adv. Eng. Soft., 81, 50-65. https://doi.org/10.1016/j.advengsoft.2014.11.003
  23. Gholizadeh, S. and Salajegheh, E. (2009), "Optimal design of structures for time history loading by swarm intelligence and an advanced metamodel", Comput. Method. Appl. Eng., 198, 2936-2949. https://doi.org/10.1016/j.cma.2009.04.010
  24. Gholizadeh, S., Salajegheh, J. and Salajegheh, E. (2009), "An intelligent neural system for predicting structural response subject to earthquakes", Adv. Eng. Soft., 40, 630-639. https://doi.org/10.1016/j.advengsoft.2008.11.008
  25. Golafshani, E.M., Rahai, A., Sebt, M.H. and Akbarpour, H. (2012), "Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic", Constr. Build. Mater., 36, 411-418. https://doi.org/10.1016/j.conbuildmat.2012.04.046
  26. He, S., Wu, Q.H., Wen, J.Y., Saunders, J.R. and Paton, R.C. (2004), "A particle swarm optimizer with passive congregation", Biosystems., 78, 135-147. https://doi.org/10.1016/j.biosystems.2004.08.003
  27. Helmy, T., Rasheed, Z. and Al-Mulhem, M. (2011), "Adaptive fuzzy logic-based framework for handling imprecision and uncertainty in classification of bioinformatics datasets", Int. J. Comput. Meth., 8, 513-534. https://doi.org/10.1142/S0219876211002496
  28. Jalal, M. and Ramezanianpour, A.A. (2012), "Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks", Compos. Part B. Eng., 43, 2990-3000. https://doi.org/10.1016/j.compositesb.2012.05.044
  29. Jang, J.S.R., Sun, C.T. and Mizulani, E. (1996), Neuro-fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence, Prentice-Hall.
  30. Juang, C.F. (2002), "A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms", IEEE. T. Fuzzy. Syst., 10, 155-170. https://doi.org/10.1109/91.995118
  31. Kashyap, J., Willis, C.R., Griffith, M.C., Ingham, J.M. and Masia, M.J. (2012), "Debonding resistance of FRP-to-clay brick masonry joints", Eng. Struct., 41, 186-198. https://doi.org/10.1016/j.engstruct.2012.03.032
  32. Kennedy, J., Eberhart, R.C. and Shi, Y. (2001), Swarm Intelligence, Morgan Kaufman Publishers.
  33. Khalifa, A., Gold, W., Nanni, A. and Abdel Aziz, M.I. (1998), "Contribution of externally bonded FRP to shear capacity of RC flexural members", J. Compos. Constr., 2, 195-202. https://doi.org/10.1061/(ASCE)1090-0268(1998)2:4(195)
  34. Khatibinia, M. and Khosravi, Sh. (2014), "A hybrid approach based on an improved gravitational search algorithm and orthogonal crossover for optimal shape design of concrete gravity dams", Appl. Soft. Comput., 16, 223-233. https://doi.org/10.1016/j.asoc.2013.12.008
  35. Khatibinia, M., Chitti, H., Akbarpour, A. and Naseri, H.R. (2016), "CShape optimization of concrete gravity dams considering dam-water-foundation interaction and nonlinear effects", Int. J. Optim. Civil Eng., 6(1), 115-34.
  36. Khatibinia, M., Fadaee, M.J., Salajegheh, J. and Salajegheh, E. (2013), "Seismic reliability assessment of RC structures including soil-structure interaction using wavelet weighted least squares support vector machine", Reliab. Eng. Syst. Saf., 110, 22-33. https://doi.org/10.1016/j.ress.2012.09.006
  37. Khatibinia, M., Gharehbagh, S. and Moustafa, A. (2015), "Seismic reliability-based design optimization of reinforced concrete structures including soil-structure interaction effects", Earthquake Engineering-From Engineering Seismology to Optimal Seismic Design of Engineering Structure, 267-304.
  38. Khatibinia, M., Salajegheh, E., Salajegheh, J. and Fadaee, M.J. (2013), "Reliability-based design optimization of RC structures including soil-structure interaction using a discrete gravitational search algorithm and a proposed metamodel", Eng. Optim., 45, 1147-1165. https://doi.org/10.1080/0305215X.2012.725051
  39. Khatibinia, M., Salajegheh, J., Fadaee, M.J. and Salajegheh, E. (2012), "Prediction of failure probability for soil-structure interaction system using modified ANFIS by hybrid of FCMFPSO", Asian. J. Civil. Eng., 13, 1-27.
  40. Khoshbin, F., Bonakdari, H., Ashraf Talesh, S.H., Ebtehaj, I., Zaji, A.H. and Azimi, H. (2016), "Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modeling the discharge coefficient in rectangular sharp-crested side weirs", Eng. Optim., 48, 933-948. https://doi.org/10.1080/0305215X.2015.1071807
  41. Mansoori, E.G., Zolghadri, M.J. and Katebi, S.D. (2008), "SGERD: a steady-state genetic algorithm for extracting fuzzy classification rules from data", IEEE. T. Fuzzy. Syst., 16(4), 1061-1071. https://doi.org/10.1109/TFUZZ.2008.915790
  42. Mansouri, I. and Kisi, O. (2015), "Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches", Compos. Part B. Eng., 70, 247-255. https://doi.org/10.1016/j.compositesb.2014.11.023
  43. Mitsuo, G. and Runnei, C. (2000), Genetic Algorithms and Engineering Optimization, John Wiley & Sons Inc.
  44. Mohammadizadeh, M.R. and Fadaee, M.J. (2009), "Torsional behavior of high-strength concrete beams strengthened using CFRP sheets; an experimental and analytical study", Int. J. Sci. Technol., 16, 321-330.
  45. Mohammadizadeh, M.R. and Fadaee, M.J. (2010), "Experimental and analytical Study on behavior of CFRP strengthened HSC beams with minimum torsional reinforcement", Iran. J. Sci. Technol., 34, 35-48.
  46. Mohammadizadeh, M.R., Fadaee, M.J. and Ronagh, H.R. (2009), "Improving torsional behavior of reinforced concrete beams strengthened with carbon fiber reinforced polymer composite", Iran. Polym. J., 18, 315-327.
  47. Oliveira, D.V., Basilio, I. and Lourenco, P.B. (2010), "Experimental bond behavior of FRP sheets glued on brick masonry", J. Compos. Constr., 15, 32-41.
  48. Ozcan, F., Atis, C.D., Karahan, O., Uncuoglu, E. and Tanyildizi, H. (2009), "Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete", Adv. Eng. Soft., 40, 856-863. https://doi.org/10.1016/j.advengsoft.2009.01.005
  49. Pan, J. and Leung, C.K.Y. (2007), "Debonding along the FRPconcrete interface under combined pulling/peeling effects", Eng. Fract. Mech., 74, 132-150. https://doi.org/10.1016/j.engfracmech.2006.01.022
  50. Perera, R., Tarazona, D., Ruiz, A. and Martin, A. (2014), "Application of artificial intelligence techniques to predict the performance of RC beams shear strengthened with NSM FRP rods. Formulation of design equations", Compos. Part B. Eng., 66, 162-173. https://doi.org/10.1016/j.compositesb.2014.05.001
  51. Plevris, V. and Asteris, P.G. (2014), "Modeling of masonry failure surface under biaxial compressive stress using Neural Networks", Constr. Build. Mater., 55, 447-461. https://doi.org/10.1016/j.conbuildmat.2014.01.041
  52. Sadrmomtazi, A., Sobhani, J. and Mirgozar. M.A. (2013), "Modeling compressive strength of EPS lightweight concrete using regression, neural network and ANFIS", Constr. Build. Mater., 42, 205-216. https://doi.org/10.1016/j.conbuildmat.2013.01.016
  53. Sayed-Ahmed, E.Y., Bakay, R. and Shrive, N.G. (2009), "Bond strength of FRP laminates to concrete: state-of-the-art review", J. Struct. Eng., 9, 45-61.
  54. Seyedpoor, S.M., Salajegheh, J., Salajegheh, E. and Gholizadeh, S. (2009), "Optimum shape design of arch dams for earthquake loading using a fuzzy inference system and wavelet neural networks", Eng. Optim., 41, 473-493 https://doi.org/10.1080/03052150802596076
  55. Shi, Y. and Eberhart, R. (1998). "A modified particle swarm optimizer", IEEE World Congress on Computational Intelligence, Evolutionary Computation Proceedings, Anchorage, AK.
  56. Shi, Y., Liu, H., Gao, L. and Zhang, G. (2011), "Cellular particle swarm optimization", Inform. Sci., 181, 4460-4493. https://doi.org/10.1016/j.ins.2010.05.025
  57. Slonski, M. (2010), "A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks", Comput. Struct., 88, 1248-1253. https://doi.org/10.1016/j.compstruc.2010.07.003
  58. Sobhani, J., Najimi, M., Pourkhorshidi, A.R. and Parhizkar, T. (2010), "Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models", Constr. Build. Mater., 24, 709-718. https://doi.org/10.1016/j.conbuildmat.2009.10.037
  59. Subbaraj, P., Rengaraj, R. and Salivahanan, S. (2011), "Enhancement of self- adaptive real-coded genetic algorithm using taguchi method for economic dispatch problem", Appl. Soft. Comput., 11, 83-92. https://doi.org/10.1016/j.asoc.2009.10.019
  60. Tsai, P.W., Hayat, T., Ahmad, B. and Chen, Ch.W. (2015), "Structural system simulation and control via NN based fuzzy model", Struc. Eng. Mech., 56(3), 385-407. https://doi.org/10.12989/sem.2015.56.3.385
  61. Willis, C.R., Yang, Q., Seracino, R. and Griffith, M.C. (2009), "Bond behavior of FRP-to-clay brick masonry joints", Eng. Struct., 31, 2580-2587. https://doi.org/10.1016/j.engstruct.2009.06.006
  62. Yuksel, S.B. and Yarar, A. (2015), "Neuro-fuzzy and artificial neural networks modeling of uniform temperature effects of symmetric parabolic haunched beams", Struc. Eng. Mech. 56(5), 787-796. https://doi.org/10.12989/sem.2015.56.5.787

피인용 문헌

  1. Efficient parameters to predict the nonlinear behavior of FRP retrofitted RC columns vol.70, pp.6, 2019, https://doi.org/10.12989/sem.2019.70.6.703