An Interval-Probability Hybrid Structural Reliability Calculation Method Based on CSSA-BR-BP

doi:10.23940/ijpe.23.10.p6.687699

Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (10): 687-699.doi: 10.23940/ijpe.23.10.p6.687699

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An Interval-Probability Hybrid Structural Reliability Calculation Method Based on CSSA-BR-BP

Yonghua Li^a,*, Shujian Liu^a, Xiaoning Bai^b, and Yufeng Wang^a

^aCollege of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian, Liaoning, China;
^bSchool of Mechanical Engineering, Dalian Jiaotong University, Dalian, Liaoning, China

Contact: * E-mail address: yonghuali@163.com
About author:Yonghua Li is a professor in the College of Locomotive and Rolling Stock Engineering at Dalian Jiaotong University. Her research interests include fatigue reliability analysis of vehicle structures, digital simulation and optimization design of mechanical products, and RAMS engineering.
Shujian Liu is a master's student in the College of Locomotive and Rolling Stock Engineering at Dalian Jiaotong University. His research interests include reliability analysis of vehicle structures.
Xiaoning Bai is a Ph.D. in the School of Mechanical Engineering at Dalian Jiaotong University. His research interests include CAE key technologies, fatigue reliability analysis of vehicle structures, uncertainty analysis, and optimization.
Yufeng Wang is a master's student in the College of Locomotive and Rolling Stock Engineering at Dalian Jiaotong University. His research interests include reliability analysis of vehicle structures.

Abstract

Abstract: For the problem of reliability analysis of hybrid structures containing interval variables and probabilistic variables, a hybrid structure reliability calculation method based on chaotic sparrow search algorithm (CSSA) and Bayesian regularized (BR) optimization algorithm optimized BP neural network is proposed. First, a reliability analysis model is constructed based on the mixed uncertainty variables, evidence theory is introduced to characterize the uncertainty of the interval variables, and the interval variables are transformed into probabilistic variables by using uniform probability processing method, which in turn decouples the two-layer nested reliability solving problem into a single-layer reliability solving problem. Next, the weights and thresholds of the BP neural network are optimized using CSSA and BR techniques, leading to the development of the CSSA-BR-BP neural network surrogate model. Finally, the HL-RF method in Advanced First Order Second Moment (AFOSM) is employed to efficiently compute the structural reliability index. The results demonstrate that the proposed method exhibits excellent fitting accuracy and enhances the efficiency of reliability analysis for hybrid structures, confirming the feasibility of the approach.

Key words: mixed uncertainty, chaotic sparrow search algorithm, BP neural network, structural reliability analysis

Yonghua Li, Shujian Liu, Xiaoning Bai, and Yufeng Wang. An Interval-Probability Hybrid Structural Reliability Calculation Method Based on CSSA-BR-BP [J]. Int J Performability Eng, 2023, 19(10): 687-699.

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References

[1] Hoseyni S.M., Hoseyni S.M., Yousefpour F., andKarimi K.Probabilistic Analysis of Containment Structural Performance in Severe Accidents. International journal of system assurance engineering and management, vol. 8, pp. 625-634, 2017.
[2] Hong L., Li H., Fu J., Li J., andPeng K.Hybrid Active Learning Method for Non-Probabilistic Reliability Analysis with Multi-Super-Ellipsoidal Model. Reliability Engineering & System Safety, vol. 222, pp. 108414, 2022.
[3] Zhao, E. and Wu, C.Long-Term Safety Assessment of Large-Scale Arch Dam based on Non-Probabilistic Reliability Analysis. In Structures, Elsevier, vol. 32, pp. 298-312, 2021.
[4] Zhi P., Li Y., andChen B.Structural Strength Analysis of Bogie Frames Considering Parameter Uncertainty. China Mechanical Engineering, vol. 30, no. 1, pp. 22, 2019.
[5] Tang Z., Peng J., Sun J., andMeng X.Non-Probabilistic Reliability Analysis of Robot Accuracy under Uncertain Joint Clearance. Machines, vol. 10, no. 10, pp. 917, 2022.
[6] Wu H. M., Cao X. S., Wang K., andCui G. H.Evaluation of Industrial Robot Motion Reliability based on Non-Probability Interval Theory. Machine Design & Research, vol. 37, no. 2, pp. 40-44, 2021.
[7] Qiao X. Z., Wang B., andPeng X. L.Non-Probabilistic Reliability-Based Shape Optimization Design of Truss Structures. Chinese Journal of Applied Mechanics, vol. 37, no. 1, pp. 176-182, 2020.
[8] Zhi P. P., Chen B. Z., Li Y. H., andSong X. P.Non-Probabilistic Reliability Analysis based on Six Sigma of MEU Bogie Frame. Journal of Railway Science and Engineering, vol. 16, no. 2, pp. 478-486, 2019.
[9] Bai X., Yu B. Y., Wang K., andZhao C. Y.Interval Non-Probabilistic Reliability Analysis of Fatigue Crack Growth in Titanium Alloys. Journal of Ship Mechanics, vol. 24, no. 8, pp. 1055-1061, 2020.
[10] Qian Y., Wang W. C., Li N., andLi X. Y.Non-Probabilistic Reliability Analysis on Composite Cylinder with High Speed based on Interval Variables. Journal of Machine Design, vol. 38, no. 12, pp. 40-46, 2021.
[11] Bo-wen, W.E.I., Liang-hong, Z.H.A.N., Huo-kun, L.I., and Zhen-kai, X.U. Non-Probabilistic Reliability Analysis of Gravity Dams based on Inversion of Interval Parameters. Chinese Journal of Geotechnical Engineering, vol. 42, no. 2, pp. 325-333, 2020.
[12] Alvarez D.A., Uribe F., andHurtado J.E.Estimation of the Lower and Upper Bounds on the Probability of Failure using Subset Simulation and Random Set Theory. Mechanical Systems and Signal Processing, vol. 100, pp. 782-801, 2018.
[13] Brevault L., Lacaze S., Balesdent M., andMissoum S.Reliability Analysis in the Presence of Aleatory and Epistemic Uncertainties, Application to the Prediction of a Launch Vehicle Fallout Zone. Journal of Mechanical Design, vol. 138, no. 11, pp. 111401, 2016.
[14] Rocca P., Anselmi N., Benoni A., andMassa A.Probabilistic Interval Analysis for the Analytic Prediction of the Pattern Tolerance Distribution in Linear Phased Arrays with Random Excitation Errors. IEEE Transactions on Antennas and Propagation, vol. 68, no. 12, pp. 7866-7878, 2020.
[15] Zhou X.Y., Wang N.W., Xiong W., Wu W.Q., andCai C.S.Multi-Scale Reliability Analysis of FRP Truss Bridges with Hybrid Random and Interval Uncertainties. Composite Structures, vol. 297, pp. 115928, 2022.
[16] Tao Q.I.U., Jianguo, Z.H.A.N.G., Jiwei, Q.I.U., Juan, W.E.I., and Lingfei, Y.O.U. Two-Parameter Optimization Design Point-Based Reliability Analysis Algorithm for Structures with Mixed Uncertainty. Acta Armamentarii, vol. 40, no. 4, pp. 865, 2019.
[17] Zheng H.W., Meng G.W., andLi F.Hybrid Reliability Analysis for Structures based on High-Order Moments and Maximum Entropy Method. JOURNAL OF MECHANICAL ENGINEERING, vol. 57, pp. 1-10, 2021.
[18] Li F., Zhou Y., Wei T., andLi H.An Interval-Probability Hybrid Reliability Method for Truck Frame. Journal of Failure Analysis and Prevention, vol. 22, no. 3, pp. 1260-1267, 2022.
[19] Wei T., Zuo W., Zheng H., andLi F.Slope Hybrid Reliability Analysis Considering the Uncertainty of Probability-Interval using Three-Parameter Weibull Distribution. Natural Hazards, vol. 105, pp. 565-586, 2021.
[20] Liu, L. and Long, X.An Efficient Reliability Analysis Method for Structures with Probability-Interval Mixed Uncertainty. Chinese Journal of Computational Mechanics, vol. 30, no. 5, pp. 605-609, 2013.
[21] Qiu T., Wang Z. C., andYou L. F.Structural Reliability Analysis Algorithm in the Presence of Random and Interval Variables. Computer Integrated Manufacturing Systems, vol. 26, no. 11, pp. 2992-3000, 2020.
[22] Hu Q. G., Yuan S. H., andWang G. S.Frequency Reliability Analysis of Connecting Rod using BP Neural Network and Mean First Order Second Moment Method.Machinery Design & Manufacture, no. 5, pp. 63-67, 2021.
[23] Jia D. W., Wu Z. Y., andHe X.A Structural Reliability Analysis Method based on the Back Propagation (BP) Neural Network and Laplace’s Method of Progressive Integration. Chinese Journal of Solid Mechanics, vol. 42, no. 4, pp. 476-489, 2021.
[24] Jiang C., Zhang Z., Han X., andLiu J.A Novel Evidence-Theory-Based Reliability Analysis Method for Structures with Epistemic Uncertainty. Computers & Structures, vol. 129, pp. 1-12, 2013.
[25] Chen N., Hu Y., Yu D., Liu J., andBeer M.A Polynomial Expansion Approach for Response Analysis of Periodical Composite Structural-Acoustic Problems with Multi-Scale Mixed Aleatory and Epistemic Uncertainties. Computer Methods in Applied Mechanics and Engineering, vol. 342, pp. 509-531, 2018.
[26] Songkun C.H.E.N. and Deyu, W.A.N.G., An Improved Monte Carlo Reliability Analysis Method based on Neural Network. Journal of Shanghai Jiaotong University, vol. 52, no. 6, pp. 687, 2018.
[27] Xue, J. and Shen, B.A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm. Systems science & control engineering, vol. 8, no. 1, pp. 22-34, 2020.

An Interval-Probability Hybrid Structural Reliability Calculation Method Based on CSSA-BR-BP

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