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Interval spectral stochastic finite element analysis of structures with aggregation of random field and bounded parameters. (English) Zbl 07870066

Summary: This paper presents the study on non-deterministic problems of structures with a mixture of random field and interval material properties under uncertain-but-bounded forces. Probabilistic framework is extended to handle the mixed uncertainties from structural parameters and loads by incorporating interval algorithms into spectral stochastic finite element method. Random interval formulations are developed based on K-L expansion and polynomial chaos accommodating the random field Young’s modulus, interval Poisson’s ratios and bounded applied forces. Numerical characteristics including mean value and standard deviation of the interval random structural responses are consequently obtained as intervals rather than deterministic values. The randomised low-discrepancy sequences initialized particles and high-order nonlinear inertia weight with multi-dimensional parameters are employed to determine the change ranges of statistical moments of the random interval structural responses. The bounded probability density and cumulative distribution of the interval random response are then visualised. The feasibility, efficiency and usefulness of the proposed interval spectral stochastic finite element method are illustrated by three numerical examples.
{Copyright © 2016 John Wiley & Sons, Ltd.}

MSC:

74-XX Mechanics of deformable solids
65-XX Numerical analysis
Full Text: DOI

References:

[1] ElishakoffI, RenYJ, ShinozukaM. Some critical observations and attendant new results in the finite element method for stochastic problems. Chaos Some critical Solitons & Fractals1996; 7:597-609.
[2] ElishakoffI, RenY. The bird’s eye view on finite element method for structures with large stochastic variations. Computer Methods in Applied Mechanics and Engineering1999; 168:51-61. · Zbl 0953.74063
[3] SchuellerGI. Computational stochastic mechanics – recent advances. Computers & Structures2001; 79:2225-2234.
[4] ElishakoffI, RenY. Finite Element Methods for Structures with Large Stochastic Variations. Oxford University Press: Oxford, New York, 2003. · Zbl 1037.74001
[5] OdenJT, BelytschkoT, BabuskaV, HughesTJR. Research directions in computational mechanics. Computer Methods in Applied Mechanics and Engineering2003; 192:913-22. · Zbl 1025.74503
[6] MatthiesHG, BrennerCE, BucherCG, SoaresCG. Uncertainties in probabilistic numerical analysis of structures and solids – stochastic finite elements. Structural Safety1997; 19:283-336.
[7] GhanemRG, SpanosPD. Stochastic Finite Elements: A Spectral Approach. Springer ‐ Verlag New York Inc.: Mineola, New York, USA, 1991. · Zbl 0722.73080
[8] ElishakoffI, RenYJ, ShinozukaM. Conditional simulation of non‐Gaussian random fields. Engineering Structures1994; 16:558-63.
[9] GhanemRG, KrugerRM. Numerical solution of spectral stochastic finite element systems. Computer Methods in Applied Mechanics and Engineering1996; 129:289-303. · Zbl 0861.73071
[10] SudretB, Der KiureghianA. Stochastic finite element methods and reliability: a state‐of‐the‐art report. In No. UCB/SEMM‐2000/08 ed, University of California, Berkeley, USA, 2000.
[11] AndersM, HoriM. Three‐dimensional stochastic finite element method for elasto‐plastic bodies. International Journal for Numerical Methods in Engineering2001; 51:449-78. · Zbl 1015.74055
[12] HuangSP, QuekST, PhoonKK. Convergence study of the truncated Karhunen-Loeve expansion for simulation of stochastic processes. International Journal for Numerical Methods in Engineering2001; 52:1029-43. · Zbl 0994.65004
[13] ChungDB, GutierrezMA, Graham‐BradyLL, LingenFJ. Efficient numerical strategies for spectral stochastic finite element models. International Journal for Numerical Methods in Engineering2005; 64:1334-49. · Zbl 1113.74065
[14] EiermannM, ErnstO, UllmannE. Computational aspects of the stochastic finite element method. Computing and Visualization in Science2007; 10:3-15. · Zbl 1123.65004
[15] StefanouG, PapadrakakisM. Assessment of spectral representation and Karhunen-Loeve expansion methods for the simulation of Gaussian stochastic fields. Computer Methods in Applied Mechanics and Engineering2007; 196:2465-77. · Zbl 1173.65302
[16] SpanosPD, BeerM, Red‐HorseJ. Karhunen‐loeve expansion of stochastic processes with a modified exponential covariance kernel. Journal of Engineering Mechanics‐Asce2007; 133:773-9.
[17] NgahMF, YoungA. Application of the spectral stochastic finite element method for performance prediction of composite structures. Composite Structures2007; 78:447-56.
[18] StefanouG. The stochastic finite element method: past, present and future. Computer Methods in Applied Mechanics and Engineering2009; 198:1031-51. · Zbl 1229.74140
[19] SpanosPD, ZeldinBA. Monte carlo treatment of random fields: a broad perspective. Applied Mechanics Reviews1998; 51:219-37.
[20] SpanosPD, ZeldinBA. Galerkin sampling method for stochastic mechanics problems. Journal of Engineering Mechanics‐Asce1994; 120:1091-106.
[21] GhanemR. Stochastic finite elements with multiple random non‐Gaussian properties. Journal of Engineering Mechanics1999; 125:26-40.
[22] GrahamLL, DeodatisG. Response and eigenvalue analysis of stochastic finite element systems with multiple correlated material and geometric properties. Probabilistic Engineering Mechanics2001; 16:11-29.
[23] StefanouG, PapadrakakisM. Stochastic finite element analysis of shells with combined random material and geometric properties. Computer Methods in Applied Mechanics and Engineering2004; 193:139-60. · Zbl 1075.74681
[24] ChenN‐Z, Guedes SoaresC. Spectral stochastic finite element analysis for laminated composite plates. Computer Methods in Applied Mechanics and Engineering2008; 197:4830-9. · Zbl 1194.74378
[25] MoensD, VandepitteD. A survey of non‐probabilistic uncertainty treatment in finite element analysis. Computer Methods in Applied Mechanics and Engineering2005; 194:1527-55. · Zbl 1137.74443
[26] BeerM, FersonS, KreinovichV. Imprecise probabilities in engineering analyses. Mechanical Systems and Signal Processing2013; 37:4-29.
[27] SchenkCA, SchuellerGI. Uncertainty Assessment of Large Finite Element Systems, Lecture Notes in Applied and Computational Mechanics. Springer‐Verlag Berlin Heidelberg: Heidelberg, Germany, 2005; 1-165. · Zbl 1101.74003
[28] ElishakoffI. Probabilistic Methods in the Theory of Structures. Wiley: New York, 1983. · Zbl 0572.73094
[29] BeckJL. Bayesian system identification based on probability logic. Structural Control & Health Monitoring2010; 17:825-47.
[30] PapadimitriouC, BeckJL, KatafygiotisLS. Updating robust reliability using structural test data. Probabilistic Engineering Mechanics2001; 16:103-13.
[31] JaynesET, BretthorstGL. Probability theory : the logic of science. Cambridge University Press: Cambridge, UK, New York NY, 2003. · Zbl 1045.62001
[32] AuSK. Connecting Bayesian and frequentist quantification of parameter uncertainty in system identification. Mechanical Systems and Signal Processing2012; 29:328-42.
[33] YanW‐J, KatafygiotisLS. A novel Bayesian approach for structural model updating utilizing statistical modal information from multiple setups. Structural Safety2015; 52:260-71.
[34] AlvarezDA, HurtadoJE. An efficient method for the estimation of structural reliability intervals with random sets, dependence modeling and uncertain inputs. Computers & Structures2014; 142:54-63.
[35] deAngelisM, PatelliE, BeerM. Advanced line sampling for efficient robust reliability analysis. Structural Safety2015; 52:170-82.
[36] BeerM, KreinovichV. Interval or moments: which carry more information?Soft Computing2013; 17:1319-27. · Zbl 1325.68225
[37] MooreRE. Interval Analysis. Prentice‐Hall: Englewood Cliffs N.J., 1966. · Zbl 0176.13301
[38] MöllerB, BeerM. Fuzzy Randomness: Uncertainty in Civil Engineering and Computational Mechanics. Springer‐Verlag Berlin Heidelberg: Heidelberg, Germany, 2004. · Zbl 1080.74003
[39] DuXP, SudjiantoA, HuangBQ. Reliability‐based design with the mixture of random and interval variables. Journal of Mechanical Design2005; 127:1068-76.
[40] GaoW, SongC, Tin‐LoiF. Probabilistic interval analysis for structures with uncertainty. Structural Safety2010; 32:191-9.
[41] JiangC, LiWX, HanX, LiuLX, LePH. Structural reliability analysis based on random distributions with interval parameters. Computers & Structures2011; 89:2292-302.
[42] MuscolinoG, SofiA. Stochastic analysis of structures with uncertain‐but‐bounded parameters via improved interval analysis. Probabilistic Engineering Mechanics2012; 28:152-63.
[43] JiangC, LongXY, HanX, TaoYR, LiuJ. Probability‐interval hybrid reliability analysis for cracked structures existing epistemic uncertainty. Engineering Fracture Mechanics2013; 112:148-64.
[44] BeerM, ZhangY, QuekST, PhoonKK. Reliability analysis with scarce information: comparing alternative approaches in a geotechnical engineering context. Structural Safety2013; 41:1-10.
[45] DoDM, GaoW, SongC, TangaramvongS. Dynamic analysis and reliability assessment of structures with uncertain‐but‐bounded parameters under stochastic process excitations. Reliability Engineering & System Safety2014; 132:46-59.
[46] WuJL, LuoZ, ZhangN, ZhangYQ. A new uncertain analysis method and its application in vehicle dynamics. Mechanical Systems and Signal Processing2015; 50‐51:659-75.
[47] MöllerB, BeerM. Engineering computation under uncertainty – capabilities of non‐traditional models. Computers & Structures2008; 86:1024-41.
[48] MollerB, GrafW, BeerM. Fuzzy structural analysis using alpha‐level optimization. Computational Mechanics2000; 26:547-65. · Zbl 1009.74053
[49] BeerM, FersonS. Fuzzy probability in engineering analyses. In Proceedings of the First International Conference on Vulnerability and Risk Analysis and Management (ICVRAM 2011) and the Fifth International Symposium on Uncertainty Modeling and Analysis (ISUMA 2011). Reston, VA: USA, 2011; 53-61.
[50] MuhannaRL, ZhangH, MullenRL. Interval finite elements as a basis for generalized models of uncertainty in engineering mechanics. Reliable Computing2007; 13:173-94. · Zbl 1206.74021
[51] DegrauweD, LombaertG, De RoeckG. Improving interval analysis in finite element calculations by means of affine arithmetic. Computers & Structures2010; 88:247-54.
[52] WuJL, LuoZ, ZhangYQ, ZhangN, ChenLP. Interval uncertain method for multibody mechanical systems using Chebyshev inclusion functions. International Journal for Numerical Methods in Engineering2013; 95:608-630. · Zbl 1352.70017
[53] BurasAJ, JaminM, LautenbacherME. A 1996 analysis of the CP violating ratio epsilon’/epsilon. Physics Letters B1996; 389:749-756.
[54] RaoSS, BerkeL. Analysis of uncertain structural systems using interval analysis. AIAA Journal1997; 35:727-735. · Zbl 0902.73082
[55] KennedyJ, EberhartR. Particle swarm optimization. In Proceedings, IEEE International Conference on Neural Networks, vol. 4: Perth, Western Australia, 1995; 1942-1948.
[56] XiaohuiH, EberhartRC, YuhuiS. Engineering optimization with particle swarm. In Swarm Intelligence Symposium, 2003 SIS ’03 Proceedings of the 2003 IEEE: Indianapolis, Indiana, USA, 2003; 53-57.
[57] ElbeltagiE, HegazyT, GriersonD. Comparison among five evolutionary‐based optimization algorithms. Advanced Engineering Informatics2005; 19:43-53.
[58] KreinovichV, NguyenHT, WuBL. On‐line algorithms for computing mean and variance of interval data, and their use in intelligent systems. Information Sciences2007; 177:3228-38. · Zbl 05174376
[59] FersonS, GinzburgL, KreinovichV, LongprL, AvilesM. Computing Variance for interval data is NP‐hard. SIGACT News2002; 33:108-18.
[60] OberkampfWL, HeltonJC, JoslynCA, WojtkiewiczSF, FersonS. Challenge problems: uncertainty in system response given uncertain parameters. Reliability Engineering & System Safety2004; 85:11-9.
[61] ElishakoffI. Notes on philosophy of the Monte Carlo method. International Applied Mechanics2003; 39:753-762. · Zbl 1121.74490
[62] OberkampfWL, DeLandSM, RutherfordBM, DiegertKV, AlvinKF. Error and uncertainty in modeling and simulation. Reliability Engineering & System Safety2002; 75:333-357.
[63] VenterG, Sobieszczanski‐SobieskiJ. Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization. Structural and Multidisciplinary Optimization2004; 26:121-131.
[64] PerezRE, BehdinanK. Particle swarm approach for structural design optimization. Computers & Structures2007; 85:1579-1588.
[65] PlevrisV, PapadrakakisM. A hybrid particle Swarm-Gradient algorithm for global structural optimization. Computer‐Aided Civil and Infrastructure Engineering2011; 26:48-68.
[66] YildizAR. A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry. Proceedings of the Institution of Mechanical Engineers Part D‐Journal of Automobile Engineering2012; 226:1340-1351.
[67] ParsopoulosKE, VrahatisMN. Particle swarm Optimizer in noisy and continuously changing environments. In Artificial Intelligence and Soft Computing, HamzaMH (ed.) (ed.). IASTED/ACTA Press; 289-294, 2001.
[68] Pant M, Thangaraj R, Grosan C, Abraham A. Improved particle swarm optimization with low‐discrepancy sequences. In IEEE World Congress on Computational Intelligence: Hong Kong, 2008; 3011-3018.
[69] LiuNG, GaoW, SongCM, ZhangN, PiYL. Interval dynamic response analysis of vehicle-bridge interaction system with uncertainty. Journal of Sound and Vibration2013; 332:3218-31.
[70] SpanosPD, GhanemR. Stochastic finite‐element expansion for random‐media. Journal of Engineering Mechanics‐Asce1989; 115:1035-53.
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