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A novel null-hypothesis approximation method of limit state function in multi-failure mode reliability analysis of structural system

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Abstract

Modern industrial products feature complex structures, multiple sub-structural systems, and high-reliability requirements, especially for aerospace products. For the reliability analysis (RA) of these products, even if the analysis error of single sub-structural systems is small, the one of global reliability may be very large, therefore, it is challenging to meet the reliability requirements for whole products. With regard to the high-reliability and multiple failure mode problems, this paper proposes a novel null-hypothesis approximation method (NHA) of limit surface function (LSF). It can obtain the points on LSF in the feasible domain. Then, a new RA framework is established by combining the convex point cloud model (CPCM) with NHA. CPCM can obtain the volume of the point cloud by focusing on the points on LSF in the feasible domain. Compared with traditional RA methods, the new RA framework is suitable for complex structural systems with high-reliability, and it can mitigate the analysis error that is caused by traditional RA methods that only concentrate on curvature in proximity to MPP. Finally, three numerical examples and an engineering example are used to test the performance of the proposed method. Results indicate that the proposed method has similar accuracy to the Monte Carlo simulation (MCS) for rare event probability, and the computational cost is acceptable for complex engineering problems.

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References

  • Barber CB, Dobkin DP, Huhdanpaa H (1996) The quickhull algorithm for convex hulls. ACM Trans Math Softw 22:4

    MathSciNet  Google Scholar 

  • Barry J (1995) Construction of three-dimensional improved-quality triangulations using local transformations. SIAM J Sci Comput 16:6

    MathSciNet  Google Scholar 

  • Ben-Haim Y, Elishakoff I (1995) Discussion on: a non-probabilistic concept of reliability. Struct Saf 17:195–199

    Google Scholar 

  • Ben-Haim Y (1995) A non-probabilistic measure of reliability of linear systems based on expansion of convex models. Struct Saf 17:91–109

    Google Scholar 

  • Ben-Haim Y (1994) A non-probabilistic concept of reliability. Struct Saf 14:227–245

    Google Scholar 

  • Bykat A (1978) Convex hull of a finite set of points in two dimensions. Inf Process Lett 7:296–298

    MathSciNet  Google Scholar 

  • Cai LX, Liu J, Jiang C, Liu GC (2022) Optimal sparse polynomial chaos expansion for arbitrary probability distribution and its application on global sensitivity analysis. Comput Methods Appl Mech Eng 399:115368

    MathSciNet  Google Scholar 

  • Cheng K, Papaioannou I, Lu ZZ, Zhang XB, Wang YP (2023) Rare event estimation with sequential directional importance sampling. Struct Saf 100:102291

    Google Scholar 

  • Deng K, Song LK, Bai GC, Li XQ (2022) Improved Kriging-based hierarchical collaborative approach for multi-failure dependent reliability assessment. Int J Fatigue 160:106842

    Google Scholar 

  • Du XP, Chen W (2004) Sequential optimization and reliability assessment method for efficient probabilistic design. J Mech Design 126:225–233

    Google Scholar 

  • Eddy WF (1977) A new convex hull algorithm for planar sets. Acm Trans Math Softw 3:398–403

    Google Scholar 

  • Elishakoff I (1995) Essay on uncertainties in elastic and viscoelastic structures: from A. M. Freudenthal’s criticisms to modern convex modeling. Comput Struct 56:871–895

    Google Scholar 

  • Eryilmaz S, Ozkut M (2020) Optimization problems for a parallel system with multiple types of dependent components. Reliab Eng Syst Saf 99:106911

    Google Scholar 

  • Fei CW, Lu C, Liem RP (2019) Decomposed-coordinated surrogate modeling strategy for compound function approximation in a turbine-blisk reliability evaluation. Aerosp Sci Technol 95:105466

    Google Scholar 

  • Gaspar B, Teixeira AP, Guedes Soares C (2017) Adaptive surrogate model with active refinement combining Kriging and a trust region method. Reliab Eng Syst Saf 165:277–291

    Google Scholar 

  • Ghazaan MI, Saadatmand F (2022) Decoupled reliability-based design optimization with a double-step modified adaptive chaos control approach. Struct Multidisc Optim 65:284

    MathSciNet  Google Scholar 

  • Guo Q, Liu YS, Chen BQ, Yao Q (2020) A variable and mode sensitivity analysis method for structural system using a novel active learning Kriging model. Reliab Eng Syst Saf 206:107285

    Google Scholar 

  • Hao P, Wang YT, Ma R, Liu HL, Wang B, Li G (2019) A new reliability-based design optimization framework using isogeometric analysis. Comput Methods Appl Mech Eng 345:476–501

    MathSciNet  Google Scholar 

  • Hong LX, Li HC, Fu JF, Li J, Peng K (2022) Hybrid active learning method for non-probabilistic reliability analysis with multi-super-ellipsoidal model. Reliab Eng Syst Saf 222:108414

    Google Scholar 

  • Jiang C, Zhang W, Han X, Ni BY, Song LJ (2015) A vine-Copula-based reliability analysis method for structures with multidimensional correlation. J Mech Eng 137:061405

    Google Scholar 

  • Jiang C, Bi RG, Lu GY, Han X (2013) Structural reliability analysis using non-probabilistic convex model. Comput Methods Appl Mech Eng 54:83–98

    MathSciNet  Google Scholar 

  • Jung Y, Cho H, Lee I (2019) Reliability measure approach for confidence-based design optimization under insufficient input data. Struct Multidisc Optim 60:1967–1982

    MathSciNet  Google Scholar 

  • Kang YJ, Noh YJ, Lim OK (2018) Development of a kernel density estimation with hybrid estimated bounded data. J Mech Sci Technol 32:5807–5815

    Google Scholar 

  • Kang Z, Luo YJ, Li A (2011) On non-probabilistic reliability-based design optimization of structures with uncertain-but-bounded parameters. Struct Saf 33:196–205

    Google Scholar 

  • Kim M, Jung Y, Lee M, Lee I (2022) An expected uncertainty reduction of reliability: adaptive sampling convergence criterion for Kriging-based reliability analysis. Struct Multidisc Optim 65:206

    MathSciNet  Google Scholar 

  • Li MY, Wang ZQ (2022) Deep reliability learning with latent adaptation for design optimization under uncertainty. Comput Methods Appl Mech Eng 397:115130

    MathSciNet  Google Scholar 

  • Ling CY, Kuo W, Xie M (2022) An overview of adaptive-surrogate-model-assisted methods for reliability-based design optimization. IEEE Trans Reliab 72:3

    Google Scholar 

  • Ling CY, Lu ZZ (2021) Support vector machine-based importance sampling for rare event estimation. Struct Multidisc Optim 63:1609–1631

    MathSciNet  Google Scholar 

  • Liu J, Meng XH, Xu C, Zhang DQ, Jiang C (2018) Forward and inverse structural uncertainty propagations under stochastic variables with arbitrary probability distributions. Comput Methods Appl Mech Eng 342:287–320

    MathSciNet  Google Scholar 

  • Lu SQ, Shi DM, Xiao H (2019) Reliability of sliding window systems with two failure modes. Reliab Eng Syst Saf 188:366-376

    Article  Google Scholar 

  • Ling CY, Lu ZZ (2021) Support vector machine-based importance sampling for rare event estimation. Struct Multidisc Optim 63:1609–1631

    MathSciNet  Google Scholar 

  • Möller B, Beer M (2008) Engineering computation under uncertainty-capabilities of non-traditional models. Comput Struct 86:1024–1041

    Google Scholar 

  • Moon MY, Cho H, Choi KK, Gaul N, Lamb D, Gorsich D (2018) Confidence-based reliability assessment considering limited numbers of both input and output test data. Struct Multidisc Optim 57:2027–2043

    MathSciNet  Google Scholar 

  • Nannapaneni S, Mahadevan S (2020) Probability-space surrogate modeling for fast multidisciplinary optimization under uncertainty. Reliab Eng Syst Saf 98:106896

    Google Scholar 

  • Nelsen RB (2006) An introduction to Copulas. Springer, New York

    Google Scholar 

  • Oberkampf WL, Helton JC, Joslyn CA, Wojtkiewicz SF, Ferson S (2004) Challenge problems: uncertainty in system response given uncertain parameters. Reliab Eng Syst Saf 85:11–19

    Google Scholar 

  • Papadrakakis M, Lagaros ND (2002) Reliability-based structural optimization using neural networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 191:3491–3507

    Google Scholar 

  • Qi YQ, Jin P, Li RZ, Zhang S, Cai GB (2020) Dynamic reliability analysis for the reusable thrust chamber: a multi-failure modes investigation based on coupled thermal-structural analysis. Reliab Eng Syst Saf 204:107080

    Google Scholar 

  • Qiao XZ, Wang B, Fang XR, Liu P (2021) Non-probabilistic reliability bounds for series structural systems. Int J Comput Methods 18:2150038

    MathSciNet  Google Scholar 

  • Shu SX, Qian JJ, Gong WH, Pi K, Yang ZQ (2023) Non-probabilistic reliability analysis of slopes based on fuzzy set theory. Appl Sci 13:7024

    Google Scholar 

  • Sklar M (1959) Fonctions de repartition a n dimensions et Leurs Marges. Publ Inst Statist Univ Paris 8:229–231

    MathSciNet  Google Scholar 

  • Song LK, Bai GC, Fei CW (2019) Multi-failure probabilistic design for turbine bladed disks using neural network regression with distributed collaborative strategy. Aerosp Sci Technol 92:464–477

    Google Scholar 

  • Song LK, Wen J, Fei CW, Bai GC (2018) Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression. Mech Syst Signal Proc 104:72–86

    Google Scholar 

  • Tian ZR, Zhi PP, Guan Y, Feng JB, Zhao YD (2023) An effective single loop Kriging surrogate method combing sequential stratified sampling for structural time-dependent reliability analysis. Structures 53:1215–1224

    Google Scholar 

  • Tian ZR, Zhi PP, Guan Y, He XH (2024) An active learning Kriging-based multipoint sampling strategy for structural reliability analysis. Qual Reliab Eng Int 40:524–549

    Google Scholar 

  • Tsompanakis Y, Papadrakakis M (2004) Large-scale reliability-based structural optimization. Struct Multidisc Optim 26:429–440

    Google Scholar 

  • Tu J, Choi KK, Park YH (1999) A new study on reliability-based design optimization. J Mech Design 121:557–564

    Google Scholar 

  • Wagner PR, Marelli S, Papaioannou I, Straub D, Sudret B (2022) Rare event estimation using stochastic spectral embedding. Struct Saf 96:102179

    Google Scholar 

  • Wang YT, Hao P, Guo ZD, Liu DC, Gao Q (2020) Reliability-based design optimization of complex problems with multiple design points via narrowed search region. J Mech Design 142:061702

    Google Scholar 

  • Wang ZQ, Wang PF (2016) Accelerated failure identification sampling for probability analysis of rare events. Struct Multidisc Optim 54:137–149

    MathSciNet  Google Scholar 

  • Wang ZY, Shafieezadeh A (2021) Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis. Struct Multidisc Optim 64:649–675

    MathSciNet  Google Scholar 

  • Wei PF, Liu FC, Tang CH (2018) Reliability and reliability-based importance analysis of structural systems using multiple response Gaussian process model. Reliab Eng Syst Saf 175:183–195

    Google Scholar 

  • Xie JY, Tian ZR, Zhi PP, Zhao ZD (2023) Reliability analysis method of coupling optimal importance sampling density and multi-fidelity Kriging model. Eksploat Niezawodn 25:161893

    Google Scholar 

  • Yang H, Feng SJ, Hao P, Ma XT, Wang B, Xu WX, Gao Q (2022) Uncertainty quantification for initial geometric imperfections of cylindrical shells: a novel bi-stage random field parameter estimation method. Aerosp Sci Technol 124:107554

    Google Scholar 

  • Yuan K, Xiao NC, Wang ZL, Shang K (2019) System reliability analysis by combining structure function and active learning kriging model. Reliab Eng Syst Saf 195:106734

    Google Scholar 

  • Zhang DQ, Shen SS, Jiang C, Han X, Li Q (2022) An advanced mixed-degree cubature formula for reliability analysis. Comput Methods Appl Mech Eng 400:115521

    MathSciNet  Google Scholar 

  • Zhang XF, Wang L, Srensen JD (2019) REIF: a novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis. Reliab Eng Syst Saf 185:440–454

    Google Scholar 

  • Zhao Y, Liu J, He ZL, Ding F (2023) Rapid dynamic analysis for structures with variable system parameters through multi-fidelity model. Comput Struct 285:107072

    Google Scholar 

  • Zhen Z, Li YL, Wen LF, Zhang Y, Wang T (2023) Reliability analysis of an embankment dam slope based on an ellipsoid model and PSO-ELM. Struct 55:2419–2432

    Google Scholar 

  • Zhi PP, Wang ZL, Chen BZ, Sheng ZQ (2022) Time-variant reliability-based multi-objective fuzzy design optimization for anti-roll torsion bar of EMU. CMES-Comp Model Eng 131:1001–1022

    Google Scholar 

  • Zhou YC, Lu ZZ, Yun WY (2020) Active sparse polynomial chaos expansion for system reliability analysis. Reliab Eng Syst Saf 202:107025

    Google Scholar 

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Funding

This work was supported by the National Natural Science Funding Foundation of China (12302145), and the National Key Research and Development Program of China (2023YFB3306700).

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Contributions

Yue Zhang: Writing-original draft, Software, Resources. Shaojun Feng: Investigation,Funding acquisition. Peng Hao: Methodology, Conceptualization. Hao Yang: Investigation. Bo Wang: Writing-review & editing. Yu Bing: Software. All authors reviewed the manuscript.

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Correspondence to Peng Hao.

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Responsible Editor: Yoojeong Noh

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Zhang, Y., Feng, S., Hao, P. et al. A novel null-hypothesis approximation method of limit state function in multi-failure mode reliability analysis of structural system. Struct Multidisc Optim 67, 139 (2024). https://doi.org/10.1007/s00158-024-03848-1

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  • DOI: https://doi.org/10.1007/s00158-024-03848-1

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