Reliability and Sensitivity Analysis of Structures Using Adaptive Neuro-Fuzzy Systems

Document Type : Regular Paper


1 Assistant Professor, Department of Civil Engineering, Payame Noor University (PNU), 19395-3697 Tehran, I.R. of Iran

2 Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, 9816745437, Iran


In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Monte Carlo simulation are applied for reliability analysis of structures. The drawback of Monte Carlo Simulation is the amount of computational efforts. ANFIS is capable of approximating structural response for calculating probability of failure, letting the computation burden at much lower cost. In fact, ANFIS derives adaptively an explicit approximation of the implicit limit state functions. To this end, a quasi-sensitivity analysis in consonance with ANFIS was developed for determination of dominant design variables, led to the approximation of the structural failure probability. However, preparation of ANFIS , was preceded using a relaxation-based method developed by which the optimum number of training samples and epochs was obtained. That was introduced to more efficiently reduce the computational time of ANFIS training. The proposed methodology was considered applying some illustrative examples.


Main Subjects

[1] Shen J., Seker O., Akbas B., Seker P., Momenzadeh S. and Faytarouni M. (2017), “Seismic performance of concentrically braced frames with and without brace buckling”, Eng. Struct., 141, 461-481.
[2] Momenzadeh, S., Kazemi M.T., Asl M.H., (2017), “Seismic performance of reduced web section moment connections”, Int. J. St. Struct., 17(2), 413-425.
[3] Ditlevsen, O. and Madsen, H.O. (1996), Structral reliability methods, John Wiley & Sons Inc., England.
[4] Haldar, A. and Mahadevan, S. (2000), Probability, reliability and statistical methods in engineering design, John Wiley & Sons, Inc., England.
[5] Nowak, A. S. and K. R. Collins (2000), Reliability of Structures, McGraw-Hill, Singapore.
[6] Lemieux, C. (2009), Monte Carlo and Quasi-Monte Carlo Sampling, Springer Science, Waterloo, Canada.
[7] Ghasemi, M.R. and Ghorbani, A. (2007), "Application of wavelet neural networks in optimization of skeletal buildings under frequency constraints", Int. J. Intel. Tech., 2(4), 223-231.
[8] Ba datli, S.M., Özkaya, E., Özyi it, H.A. and Tekin, A. (2009), "Nonlinear vibrations of stepped beam systems using artificial neural networks", Struct. Eng. Mech., 33(1), 11-24.
[9] Papadrakakis, M., Papadopoulos, V. and Lagaro, N. (1996), "Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation", Comp. Meth. Appl. Mech. Eng., 63, 136-145.
[10] Cardoso, J.B., Almeida, J.R., Dias, J.M. and Coelho, P.G. (2007), "Structural reliability analysis using Monte Carlo simulation and neural networks", Comp. Struct., 39(1), 503-513
[11] Godjevac, J. (1993), "State of the art in the neuro fuzzy field", Ecole Polytechnique Fédérale de Lausanne, Département d’Informatique, Laboratoire de Microinformatique, Technical report, 93.25, 1-18.
[12] Fu, J.Y., Li, Q.S. and Xie, Z.N. (2006), "Prediction of wind loads on a large flat roof using fuzzy neural networks", Eng. Struct., 28, 153-161.
[13] Fonseca, E. T. and Vellasco, P. C. G. (2007), "A neuro-fuzzy evaluation of steel beams patch load behavior", Adv. Eng. Soft., 39, 558-572.
[14] Topcu, I. E. and Sardemir, M. (2007), "Prediction of rubberized concrete properties using artificial neural network and fuzzy logic" Constr. Build. Mat., 22, 532-540.
[15] Topcu, I. E. and Sardemir, M. (2008), "Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic", Comp. Mat. Sci., 41, 305-311.
[16] Faravelli, L. and Yao, T. (1996), "Use of adaptive networks in fuzzy control of civil structures", Microcomp. Civil Eng., 11, 67-76.
[17] Ghorbani, A. and Ghasemi, M.R. (2009), "Reliability based optimization of truss structures using neuro-fuzzy systems", Proceedings of 8th World Congress on Structural and Multidisciplinary Optimization, Lisbon, Portugal, June.
[18] Melchers, R. E. and Ahmed, M. (2004), "A fast approximation method for parameter sensitivity estimation in Monte Carlo structural reliability", Comp. Struct, 82, 55-61.
[19] Song, J. and Kang, W.H. (2009), "System reliability and sensitivity under statistical dependence by matrix-based system reliability method", Struct. Safety, 31, 148-156.
[20] Marseguerra, M., Masini, R., Zio, E. and Cojazzi, G. (2003), "Variance decomposition-based sensitivity analysis via neural networks", Reliab. Eng. Syst. Safe., 79, 229-238.
[21] Mahadevan, S. (1996), Monte Carlo Simulation in Reliability-Based Mechanical Design, Marcel Dekker, NY.
[22] Zadeh, L.A. (1965), "Fuzzy sets", Inf. Contr., 8(3), 338-353.
[23] Sugeno, M. (1985), Industrial applications of fuzzy control, Elsevier Science Pub. Co.
[24] Wang, Y. M. and Elhag, T. (2008), "An adaptive neuro-fuzzy inference system for bridge risk assessment", Expert Syst. Appl., 34, 3099-3106.
[25] Jang, J.S.R. (1993), "ANFIS: Adaptive-network-based fuzzy inference systems", IEEE, Trans. Syst. Man Cyber., 23, 665–685.
[26] Saltteli, A., Tarantola, S., Compolongo, F. and Ratto, M. (2004), Sensitivity analysis in practice, John Wiley & Sons Inc., England.
[27] Kiureghian, A. D., Lin, H. Z., and Hwang, S. J. (1987), "Second-order reliability approximations", J. Eng. Mech., ASCE, 113(8), 1208-1225.
[28] Cheng, J. (2007), "Hybrid genetic algorithms for structural reliability analysis", Comp. Struct., 85, 1524-1533.