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Structural reliability assessment based on subjective uncertainty. (English) Zbl 07488886

Summary: In traditional structural reliability analysis, the uncertainties, such as loads and strengths, are considered as random variables with specific probability distributions. When the information is insufficient, it is difficult to obtain the distribution functions. Hence, experts are usually asked to estimate the belief degrees. Uncertainty theory is a branch of mathematics used to model the belief degrees of experts. In this paper, the factors influencing system structures are treated as independent uncertain variables. Based on that, the concepts of safety margin, structural reliability and failure belief degree are proposed, respectively. Then the general calculation methods of structural reliability and failure belief degree for monotonic safety margins are given, which are not restricted by the specific distributions of variables and the safety margin forms. Furthermore, the reliability index is given for the structure with linear safety margin and normal uncertain variables, and the relationship between structural reliability and reliability index is proposed. In addition, the geometric property of reliability index is investigated and used to define the reliability index in non-linear safety margin and normal uncertain variables case. Finally, several numerical examples are employed to demonstrate the effectiveness of the methods.

MSC:

62-XX Statistics
74-XX Mechanics of deformable solids
Full Text: DOI

References:

[1] Christensen, P. T. and Baker, M. J. [1982] Structural Reliability Theory and Its Applications (Springer-Verlag, Berlin). · Zbl 0495.73078
[2] Duan, S. Y., Han, X. and Liu, G. R. [2019] “ Structural optimization and reliability analysis of automotive composite bumper against low-velocity longitudinal and corner pendulum impacts,” Int. J. Comput. Methods16, 1841003. · Zbl 1489.74044
[3] Hasofer, A. M. and Lind, N. C. [1974] “ An exact and invariant first-order reliability format,” J. Eng. Mech. Div.100, 111-121.
[4] James, M. N. [2011] “ Residual stress influences on structural reliability,” Eng. Failure Anal.18, 1909-1920.
[5] Jiang, C., Li, W. X., Han, X., Liu, L. X. and Le, P. H. [2011] “ Structural reliability analysis based on random distributions with interval parameters,” Comput. Struct.89, 2292-2302.
[6] Ke, H. and Yao, K. [2016] “ Block repalcement policy in uncertain environment,” Reliab. Eng. Syst. Safety148, 119-124.
[7] Kiureghian, A. D. [1991] “ Bayesian analysis of model uncertainty in structural reliability,” Reliab. Opt. Struct. Syst.61, 211-221. · Zbl 0765.60085
[8] Kiureghian, A. D. and Ke, J. B. [1988] “ The stochastic finite element method in structural reliability,” Prob. Eng. Mech.3, 83-91.
[9] Li, G. J., Lu, Z. Z. and Jia, X. [2015] “ A fuzzy reliability approach for structures based on the probability perspective,” Struct. Safety54, 10-18.
[10] Li, X. Y., Wu, J. P., Liu, L., Wen, M. L. and Kang, R. [2019] “ Modeling accelerated degradation data based on the uncertain process,” IEEE Trans. Fuzzy Syst.27, 1532-1542.
[11] Liu, B. [2012] “ Why is there a need for uncertainty theory?” J. Uncertain Syst.6, 3-10.
[12] Liu, B. [2007] Uncertainty Theory, 2nd edn. (Springer-Verlag, Berlin). · Zbl 1141.28001
[13] Liu, B. [2010a] Uncertainty Theory \(:\) A Branch of Mathematics for Modeling Human Uncertainty (Springer-Verlag, Berlin).
[14] Liu, B. [2010b] “ Uncertain risk analysis and uncertain reliability analysis,” J. Uncertain Syst.4, 163-170.
[15] Liu, P. L. and Kiureghian, A. D. [1991] “ Optimization algorithms for structural reliability,” Struct. Safety9, 161-177.
[16] Liu, T. S. and Huang, G. R. [1992] “ Fatigue reliability of structures based on probability and possibility measures,” Comput. Struct.45, 361-368. · Zbl 0775.73354
[17] Liu, Y., Ma, Y., Qu, Z. G. and Li, X. Z. [2018] “ Reliability mathematical models of repairable systems with uncertain lifetimes and repair times,” IEEE Access6, 71285-71295.
[18] Liu, Y., Qu, Z. G., Li, X. Z., An, Y. and Yin, W. L. [2019] Reliability modelling for repairable systems with stochastic lifetimes and uncertain repair times, IEEE Trans. Fuzzy Syst.27, 2396-2405.
[19] Madsen, H. O., Krenk, S. and Lind, N. C. [1986] Methods of Structural Safety (Prentice-Hall, Englewood Cliffs, New Jersey).
[20] Möller, B., Graf, W. and Beer, M. [2003] “ Safety assessment of structures in view of fuzzy randomness,” Comput. Struct.81, 1567-1582.
[21] Ni, Z. and Qiu, Z. P. [2010] “ Hybrid probabilistic fuzzy and non-probabilistic model of structural reliability,” Comput. Indus. Eng.58, 463-467.
[22] Rackwitz, R. and Flessler, B. [1978] “ Structural reliability under combined random load sequences,” Comput. Struct.9, 489-494. · Zbl 0402.73071
[23] Savoia, M. [2002] “ Structural reliability analysis through fuzzy number approach with application to stability,” Comput. Struct.80, 1087-1102.
[24] Sawyer, J. P. and Rao, S. S. [1999] “ Strength-based reliability and fracture assessment of fuzzy mechanical and structural systems,” AIAA J.37, 84-92.
[25] Sun, W. C. and Yang, Z. C. [2014] “ A non-probabilistic model of structural reliability based on fuzzy convex set model,” Sci. Sin.44, 935-943.
[26] Tang, Z. C., Lu, Z. Z. and Xia, Y. J. [2013] “ Numerical method for fuzzy reliability analysis,” J. Aircraft50, 1710-1715.
[27] Vo-Duy, T., Duong-Gia, D., Ho-Huu, V. and Nguyen-Thoi, T. [2020] “ An effective couple method for reliability-based multi-objective optimization of truss structures with static and dynamic constraints,” Int. J. Comput. Methods17, 1950016. · Zbl 07205471
[28] Wang, G. Y. and Wang, W. Q. [1986] “ Fuzzy reliability analysis of aseismic structures,” Acta Mech. Sinica2, 322-332.
[29] Wang, L., Grandhi, R. V. and Hopkins, D. A. [1994] “ Structural reliability optimization using an efficient safety index calculation procedure,” Int. J. Numer. Methods Eng.38, 1721-1738. · Zbl 0822.73052
[30] Wang, L., Liu, Y. R. and Liu, Y. S. [2019] “ An inverse method for distributed dynamic load identification of structures with interval uncertainties,” Adv. Eng. Softw.131, 77-89.
[31] Wang, L. and Liu, Y. R. [2020] “ A novel method of distributed dynamic load identification for aircraft structure considering multi-source uncertainties,” Struct. Multidisciplinary Opt.61, 1929-1952.
[32] Wang, L., Liu, Y. R. and Liu, Y. S. [2020] “ The optimal controller design framework for PID-based vibration active control systems via non-probabilistic time-dependent reliability measure,” ISA Trans.105, 129-145.
[33] Wang, P. D., Zhang, J. G., Zhai, H. and Qiu, J. W. [2017] “ A new structural reliability index based on uncertainty theory,” Chin. J Aeronaut.30, 1451-1458.
[34] Wang, Y. T., Hao, P., Yang, H., Wang, B. and Gao, Q. [2020] “ A confidence-based reliability optimization with single loop strategy and second-order reliability method,” Comput. Methods Appl. Mech. Eng.372, 113436. · Zbl 1506.74252
[35] Yao, K. and Ralescu, D. [2013] “ Age replacement policy in uncertain environment,” Iran. J. Fuzzy Syst.10, 29-39. · Zbl 1331.60176
[36] Zadeh, L. [1965] “ Fuzzy sets,” Inform.Control8, 338-353. · Zbl 0139.24606
[37] Zhai, H. and Zhang, J. G. [2019] “ Equilibrium reliability measure for structural design under twofold uncertainty,” Inform. Sci.477, 466-489. · Zbl 1450.62130
[38] Zhang, J., Ma, X. and Yu, Z. [2017] “ A stress-strength time-varying correlation interference model for structural reliability analysis using copulas,” IEEE Trans. Reliab.66, 351-365.
[39] Zhang, L. X., Meng, X. J. and Zhang, H. [2020] “ Reliability-based design optimization for design problems with random fuzzy and interval uncertainties,” Int. J. Comput. Methods17, 1950018. · Zbl 07205473
[40] Zhao, Y. G. and Ono, T. [2001] “ Moment methods for structural reliability,” Struct. Safety23, 47-75.
[41] Zeng, Z., Kang, R., Wen, M. and Zio, E. [2018] “ Uncertainty theory as a basis for belief reliability,” Inform. Sci.429, 26-36. · Zbl 1436.62482
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