Gradient Optimization Assisted Bubble Sampling Method for Structural Reliability Analysis
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OpenAlex topics: Probabilistic and Robust Engineering Design · Reliability and Maintenance Optimization · Risk and Portfolio Optimization
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References (69)
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1. Zhao H, Yue Z, Liu Y, Gao Z, Zhang Y. An efficient reliability method combining adaptive importance sampling and kriging metamodel. Appl Math Model. 2015;39:1853–66.10.1016/j.apm.2014.10.015 ↗
2. Keshtegar B, Lee I. Relaxed performance measure approach for reliability-based design optimization. Struct Multidiscip Optim. 2016;54:1439–54.10.1007/s00158-016-1561-8 ↗
3. Bradley BA, Lee DS. Accuracy of approximate methods of uncertainty propagation in seismic loss estimation. Struct Saf. 2010;32:13–24.10.1016/j.strusafe.2009.04.001 ↗
4. Xiang Y, Liu Y. Application of inverse first-order reliability method for probabilistic fatigue life prediction. Probabilistic Eng Mech. 2011;26:148–56.10.1016/j.probengmech.2010.11.001 ↗
5. Low YM. A new distribution for fitting four moments and its applications to reliability analysis. Struct Saf. 2013;42:12–25.10.1016/j.strusafe.2013.01.007 ↗
6. Yadav V, Rahman S. Adaptive-sparse polynomial dimensional decomposition methods for high-dimensional stochastic computing. Comput Methods Appl Mech Eng. 2014;274:56–83.10.1016/j.cma.2014.01.027 ↗
7. Teng D, Feng YW, Lu C, Liu JQ, Chen JY. Vectorial generative adversarial surrogate modeling reliability evaluation framework for engineering structural systems. Reliab Eng Syst Saf. 2024;247:110076.10.1016/j.ress.2024.110076 ↗
8. Lee TH, Jung JJ. A sampling technique enhancing accuracy and efficiency of metamodel-based RBDO: constraint boundary sampling. Comput Struct. 2008;86:1463–76.10.1016/j.compstruc.2007.05.023 ↗
9. Jiang C, Qiu H, Gao L, Wang D, Yang Z, Chen L. Real-time estimation error-guided active learning kriging method for time-dependent reliability analysis. Appl Math Model. 2020;77:82–98.10.1016/j.apm.2019.06.035 ↗
10. Tabandeh A, Jia G, Gardoni P. A review and assessment of importance sampling methods for reliability analysis. Struct Saf. 2022;97:102216.10.1016/j.strusafe.2022.102216 ↗
11. Hu Y, Lu Z, Wei N, Jiang X, Safety S. Importance sampling enhanced by adaptive two-stage kriging model and active subspace for analyzing rare probability with high dimensional input vector. Reliab Eng Syst Saf. 2024;245:110019.10.1016/j.ress.2024.110019 ↗
12. Behrendt M, Lyu MZ, Luo Y, Chen J-B, Beer M. Failure probability estimation of dynamic systems employing relaxed power spectral density functions with dependent frequency modeling and sampling. Probabilistic Eng Mech. 2024;75:103592.10.1016/j.probengmech.2024.103592 ↗
13. Lyu MZ, Feng DC, Chen JB, Li J. A decoupled approach for determination of the joint probability density function of a high-dimensional nonlinear stochastic dynamical system via the probability density evolution method. Comput Methods Appl Mech Eng. 2024;418:116443.10.1016/j.cma.2023.116443 ↗
14. Low B, Tang WH. Efficient spreadsheet algorithm for first-order reliability method. J Eng Mech. 2007;133:1378–87.
15. Gholami PJ. Fatigue reliability analysis of fiber-reinforced laminated composites by continuum damage mechanics. Acta Mech Solida Sin. 2023;36:469–76.10.1007/s10338-023-00385-0 ↗
16. Fiessler B, Neumann HJ, Rackwitz R. Quadratic limit states in structural reliability. Journal of the Engineering Mechanics Division. 1979;105:661–76.10.1061/JMCEA3.0002512 ↗
17. Zhang Z, Jiang C, Wang G, Han X. First and second order approximate reliability analysis methods using evidence theory. Reliab Eng Syst Saf. 2015;137:40–9.10.1016/j.ress.2014.12.011 ↗
18. Acar E, Rais-Rohani M, Eamon CD. Reliability estimation using univariate dimension reduction and extended generalised lambda distribution. Int J Reliab Saf. 2010;4:166–87.10.1504/IJRS.2010.032444 ↗
19. Zhao YG, Ono T. Moment methods for structural reliability. Struct Saf. 2001;23:47–75.10.1016/S0167-4730(00)00027-8 ↗
20. Lee I, Shin J, Choi K. Equivalent target probability of failure to convert high-reliability model to low-reliability model for efficiency of sampling-based RBDO. Struct Multidiscip Optim. 2013;48:235–48.10.1007/s00158-013-0905-x ↗
21. Hong L, Li H, Fu J. A novel surrogate-model based active learning method for structural reliability analysis. Comput Methods Appl Mech Eng. 2022;394:114835.10.1016/j.cma.2022.114835 ↗
22. Lieu QX, Nguyen KT, Dang KD, Lee S, Kang J, Lee J. An adaptive surrogate model to structural reliability analysis using deep neural network. Expert Syst Appl. 2022;189:116104.10.1016/j.eswa.2021.116104 ↗
23. Xiao NC, Yuan K, Zhou C. Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables. Comput Methods Appl Mech Eng. 2020;359:112649.10.1016/j.cma.2019.112649 ↗
24. Xu C, Yang Y, Wu H, Zhou J. A new active learning method for system reliability analysis with multiple failure modes. Reliab Eng Syst Saf. 2023;240:109614.10.1016/j.ress.2023.109614 ↗
25. Oliver MA, Webster R. Kriging: a method of interpolation for geographical information systems. International journal of geographical information systems. 1990;4:313–32.10.1080/02693799008941549 ↗
26. Echard B, Gayton N, Lemaire M. AK-MCS: an active learning reliability method combining kriging and Monte Carlo simulation. Struct Saf. 2011;33:145–54.10.1016/j.strusafe.2011.01.002 ↗
27. Wang D, Qiu H, Gao L, Jiang C. A subdomain uncertainty-guided kriging method with optimized feasibility metric for time-dependent reliability analysis. Reliab Eng Syst Saf. 2024;243:109839.10.1016/j.ress.2023.109839 ↗
28. Zhang D, Zhou P, Jiang C, Yang M, Han X, Li Q. A stochastic process discretization method combing active learning kriging model for efficient time-variant reliability analysis. Comput Methods Appl Mech Eng. 2021;384:113990.10.1016/j.cma.2021.113990 ↗
29. Shi Y, Beer M. Physics-informed neural network classification framework for reliability analysis. Expert Syst Appl. 2024;258:125207.10.1016/j.eswa.2024.125207 ↗
30. Huang CH, Huang DH, Lin YK. Network reliability prediction for random capacitated-flow networks via an artificial neural network. Reliab Eng Syst Saf. 2023;237:109378.10.1016/j.ress.2023.109378 ↗
31. Wang J, Li C, Xu G, Li Y, Kareem A. Efficient structural reliability analysis based on adaptive Bayesian support vector regression. Comput Methods Appl Mech Eng. 2021;387:114172.10.1016/j.cma.2021.114172 ↗
32. Feng J, Liu L, Wu D, Li G, Beer M, Gao W. Dynamic reliability analysis using the extended support vector regression (X-SVR). Mech Syst Signal Process. 2019;126:368–91.10.1016/j.ymssp.2019.02.027 ↗
33. Zhang D, Han X, Jiang C, Liu J, Li Q. Time-dependent reliability analysis through response surface method. J Mech Des. 2017;139:041404.10.1115/1.4035860 ↗
34. Dubourg V, Sudret B. Meta-model-based importance sampling for reliability sensitivity analysis. Struct Saf. 2014;49:27–36.10.1016/j.strusafe.2013.08.010 ↗
35. Xiang Z, Chen J, Bao Y, Li H. An active learning method combining deep neural network and weighted sampling for structural reliability analysis. Mech Syst Signal Process. 2020;140:106684.10.1016/j.ymssp.2020.106684 ↗
36. Jiang C, Qiu H, Yang Z, Chen L, Gao L, Li P. A general failure-pursuing sampling framework for surrogate-based reliability analysis. Reliab Eng Syst Saf. 2019;183:47–59.10.1016/j.ress.2018.11.002 ↗
37. Ding C, Wei P, Shi Y, Liu J, Broggi M, Beer M. Sampling and active learning methods for network reliability estimation using k-terminal spanning tree. Reliab Eng Syst Saf. 2024;250:110309.10.1016/j.ress.2024.110309 ↗
38. Stavroulakis G, Giovanis DG, Papadrakakis M, Papadopoulos V. A new perspective on the solution of uncertainty quantification and reliability analysis of large-scale problems. Comput Methods Appl Mech Eng. 2014;276:627–58.10.1016/j.cma.2014.03.009 ↗
39. Yeh WC, Lin YC, Chung YY. Performance analysis of cellular automata Monte Carlo simulation for estimating network reliability. Expert Syst Appl. 2010;37:3537–44.10.1016/j.eswa.2009.09.070 ↗
40. Xiao NC, Yuan K, Zhan H. System reliability analysis based on dependent kriging predictions and parallel learning strategy. Reliab Eng Syst Saf. 2022;218:108083.10.1016/j.ress.2021.108083 ↗
41. Rubinstein RY, Kroese DP. Simulation and the Monte Carlo method. Hoboken: Wiley; 2016.10.1002/9781118631980 ↗
42. Rashki M. The soft Monte Carlo method. Appl Math Model. 2021;94:558–75.10.1016/j.apm.2021.01.022 ↗
43. Engelund S, Rackwitz R. A benchmark study on importance sampling techniques in structural reliability. Struct Saf. 1993;12:255–76.10.1016/0167-4730(93)90056-7 ↗
44. Ibrahim Y. Observations on applications of importance sampling in structural reliability analysis. Struct Saf. 1991;9:269–81.10.1016/0167-4730(91)90049-F ↗
45. Papaioannou I, Breitung K, Straub D. Reliability sensitivity estimation with sequential importance sampling. Struct Saf. 2018;75:24–34.10.1016/j.strusafe.2018.05.003 ↗
46. Au SK, Beck JL. Estimation of small failure probabilities in high dimensions by subset simulation. Probabilistic Eng Mech. 2001;16:263–77.10.1016/S0266-8920(01)00019-4 ↗
47. Rashki M, Miri M, Moghaddam MA. A new efficient simulation method to approximate the probability of failure and most probable point. Struct Saf. 2012;39:22–9.10.1016/j.strusafe.2012.06.003 ↗
48. Gillespie DT, Roh M, Petzold LR. Refining the weighted stochastic simulation algorithm. J Chem Phys. 2009. https://doi.org/10.1063/1.3116791.10.1063/1.3116791 ↗
49. Thedy J, Liao KW. Multisphere-based importance sampling for structural reliability. Struct Saf. 2021;91:102099.10.1016/j.strusafe.2021.102099 ↗
50. Peherstorfer B, Cui T, Marzouk Y, Willcox K. Multifidelity importance sampling. Comput Methods Appl Mech Eng. 2016;300:490–509.10.1016/j.cma.2015.12.002 ↗
51. Dai H, Zhang H, Rasmussen KJ, Wang W. Wavelet density-based adaptive importance sampling method. Struct Saf. 2015;52:161–9.10.1016/j.strusafe.2014.02.003 ↗
52. Li Z, Lu Z. An adaptive optimal importance sampling method for efficiently calibrating augmented failure probability. Struct Multidiscip Optim. 2025;68:6.10.1007/s00158-024-03923-7 ↗
53. Li H, Lu Z, Feng K. A double-loop kriging model algorithm combined with importance sampling for time-dependent reliability analysis. Eng Comput. 2024;40:1539–58.10.1007/s00366-023-01879-8 ↗
54. Li H-S, Cao Z-J. Matlab codes of subset simulation for reliability analysis and structural optimization. Struct Multidiscip Optim. 2016;54:391–410.10.1007/s00158-016-1414-5 ↗
55. Au S-K, Beck JL. Subset simulation and its application to seismic risk based on dynamic analysis. J Eng Mech. 2003;129:901–17.
56. Okasha NM. An improved weighted average simulation approach for solving reliability-based analysis and design optimization problems. Struct Saf. 2016;60:47–55.10.1016/j.strusafe.2016.01.005 ↗
57. Xu J, Kong F. A new unequal-weighted sampling method for efficient reliability analysis. Reliab Eng Syst Saf. 2018;172:94–102.10.1016/j.ress.2017.12.007 ↗
58. Wei N, Lu Z, Hu Y. An eccentric radial-based importance sampling method for reliability analysis. Expert Syst Appl. 2023. https://doi.org/10.1016/j.eswa.2023.119687.10.1016/j.eswa.2023.119687 ↗
59. Lee I, Choi KK, Gorsich D. System reliability-based design optimization using the MPP-based dimension reduction method. Struct Multidiscip Optim. 2010;41:823–39.10.1007/s00158-009-0459-0 ↗
60. Meng Z, Li C, Pang Y, Li G, He W. New bubble sampling method for reliability analysis. Struct Multidiscip Optim. 2023;66:180.10.1007/s00158-023-03626-5 ↗
61. Depina I, Le TMH, Fenton G, Eiksund G. Reliability analysis with metamodel line sampling. Struct Saf. 2016;60:1–15.10.1016/j.strusafe.2015.12.005 ↗
62. Naess A, Leira BJ, Batsevych O. System reliability analysis by enhanced Monte Carlo simulation. Struct Saf. 2009;31:349–55.10.1016/j.strusafe.2009.02.004 ↗
63. Meng Z, Li G, Yang D, Zhan L. A new directional stability transformation method of chaos control for first order reliability analysis. Struct Multidiscip Optim. 2017;55:601–12.10.1007/s00158-016-1525-z ↗
64. Cox JC, Sadiraj V. On the coefficient of variation as a criterion for decision under risk. J Math Psychol. 2010;54:387–94.10.1016/j.jmp.2010.01.002 ↗
65. Bichon BJ, McFarland JM, Mahadevan S. Efficient surrogate models for reliability analysis of systems with multiple failure modes. Reliab Eng Syst Saf. 2011;96:1386–95.10.1016/j.ress.2011.05.008 ↗
66. Du X. System reliability analysis with saddlepoint approximation. Struct Multidiscip Optim. 2010;42:193–208.10.1007/s00158-009-0478-x ↗
67. Keshtegar B, Zhu SP. Three-term conjugate approach for structural reliability analysis. Appl Math Model. 2019;76:428–42.10.1016/j.apm.2019.06.022 ↗
68. Zhang X, Shao Y, Xing Y. Calculation and analysis of landing double rocker arms holding pole based on finite element simulation. J Phys: Conf Ser. 2021. https://doi.org/10.1088/1742-6596/2083/3/032017.10.1088/1742-6596/2083/3/032017 ↗
69. Qian J, Yi J, Cheng Y, Liu J, Zhou Q. A sequential constraints updating approach for kriging surrogate model-assisted engineering optimization design problem. Eng Comput. 2020;36:993–1009.10.1007/s00366-019-00745-w ↗