Reliability-based Design Optimization of Crane Metal Structure Based on Constraint Boundary Sampling

doi:10.3901/JME.2023.08.288

Abstract

Abstract: The reliability-based design optimization(RBDO) for crane metal structures(CMSs) based on the Kriging surrogate models(KSMs) in the whole region, to ensure the correctness of the optimization results, a large number of high-fidelity samples are still required, which results in a high computational cost. A CMSs' RBDO based on KSMs of the constraint boundary sampling(CBS)is proposed. According to this sampling rule, only more high-fidelity samples are drawn in the constraint boundary region, which improves the fitting accuracy of the constraint boundary region and reduces the time consumption caused by constructing high-precision surrogate models in unconstrained boundary regions. Comparison studies with the RBDO for CMSs based on KSMs of sampling in the whole region indicated that the proposed approach could save the calculation cost by about 78% under the same optimization results. The CBS method could enhance the design efficiency greatly, meanwhile maintaining good accuracy in the RBDO based on KSMs.

Key words: crane metal structure, Kriging surrogate model, constraint boundary sampling, reliability-based design optimization

CLC Number:

TH215

FAN Xiao-ning, WANG Kai, YU Chang. Reliability-based Design Optimization of Crane Metal Structure Based on Constraint Boundary Sampling[J]. Journal of Mechanical Engineering, 2023, 59(8): 288-298.

References

[1] LAGAROS N D,PAPADRAKAKIS M. Applied soft computing for optimum design of structures[J]. Structural and Multidisciplinary Optimization,2012,45(6):787-799.
[2] LIU P,XING L,LIU Y,et al. Strength analysis and optimal design for main girder of double-trolley overhead traveling crane using finite element method[J]. Journal of Failure Analysis&Prevention,2014,14(1):76-86.
[3] QU X,XU G,FAN X,et al. Intelligent optimization methods for the design of an overhead travelling crane[J].Chinese Journal of Mechanical Engineering,2015,28(1):187-196.
[4] QI Q,XU G,FAN X,et al. A new specular reflection optimization algorithm[J]. Advances in Mechanical Engineering,2015,7(10):261-270.
[5] FAN X,BI X. Reliability-based design optimization for crane metallic structure using ACO and AFOSM based on China standards[J]. Mathematical Problems in Engineering,2015,4:828930.
[6] FAN X,ZHI B. Design for a crane metallic structure based on imperialist competitive algorithm and inverse reliability strategy[J]. Chinese Journal of Mechanical Engineering,2017,30(4):900-912.
[7] PAPADOPOULOS V,GIOVANIS D G,LAGAROS N D,et al. Accelerated subset simulation with neural networks for reliability analysis[J]. Computer Methods in Applied Mechanics and Engineering,2012,223-224:70-80.
[8] JU B H,LEE B C. Reliability-based design optimization using a moment method and a Kriging metamodel[J].Engineering Optimization,2008,40(5):421-438.
[9] FAN X,WANG P,HAO F. Reliability-based design optimization of crane bridges using Kriging-based surrogate models[J]. Structural and Multidisciplinary Optimization,2019,59(3):993-1005.
[10] WANG Z, SHAFIEEZADEH A. REAK:Reliability analysis through error rate-based adaptive Kriging[J].Reliability Engineering&System Safety,2019,182:33-45.
[11] 龙腾,李学亮,黄波,等.基于自适应代理模型的翼型气动隐身多目标优化[J].机械工程学报,2016,52(22):101-111.LONG Teng, LI Xueliang, HUANG Bo, et al.Aerodynamic and stealthy performance optimization of airfoil based on adaptive surrogate model,Journal of Mechanical Engineering,2016,52(22):101-111.
[12] LEE T H,JUNG J J. A sampling technique enhancing accuracy and efficiency of metamodel-based RBDO:Constraint boundary sampling[J]. Computers&Structures,2008,86(13-14):1463-1476.
[13] ZHUANG X,PAN R. A sequential sampling strategy to improve reliability-based design optimization with implicit constraint functions[J]. Journal of Mechanical Design,2012,134(2):021002-021011.
[14] CHEN Z,PENG S,LI X,et al. An important boundary sampling method for reliability-based design optimization using kriging model[J]. Structural&Multidisciplinary Optimization,2015,52(1):55-70.
[15] ZENG M,ZHANG D,LIU Z,et al. An adaptive directional boundary sampling method for efficient reliability-based design optimization[J]. Journal of Mechanical Design,2018,140(12):1-12.
[16] MOUSTAPHA M, SUDRET B. Surrogate-assisted reliability-based design optimization:a survey and a new general framework[J]. Structural and Multidisciplinary Optimization,2019,60(5):2157-2176.
[17] ZHANG J,XIAO M,LI P,et al. Sampling-based system reliability-based design optimization using composite active learning Kriging[J]. Computers&Structures,2020,239(106321):1-12.
[18] 曾锋,周金柱.集成最小化置信下限和信赖域的动态代理模型优化策略[J].机械工程学报,2017,53(13):170-178.ZENG Feng,ZHOU Jinzhu. Optimization strategy for dynamic metamodel integrating minimize lower confidence bound and trust region[J]. Journal of Mechanical Engineering,2017,53(13):170-178.
[19] 黄晓旭,陈建桥.基于主动学习Kriging模型的可靠性分析[J].固体力学学报,2016,37(2):172-180.HUANG Xiaoxu,CHEN Jianqiao. Reliability analysis based on active learning Kriging model[J]. Chinese Journal of Solid Mechanics,2016,37(2):172-180.
[20] 中国标准出版社. GB/T3811–2008起重机设计规范[S].北京:中国标准出版社,2008.China Standards Press. GB/T3811–2008 Design rules for cranes[S]. Beijing:China Standards Press,2008.
[21] ROSENBLATT M. Remarks on a multivariate transformation[J]. Annals of Mathematical Statistics,1952,23(3):470-472.
[22] LIU P L,der KIUREGHIAN A. Multivariate distribution models with prescribed marginals and covariances[J].Engineering Mechanics,1986,1(2):105-112.
[23] NIKOLAIDIS E, BURDISSO R. Reliability based optimization:A safety index approach[J]. Computers&Structures,1988,28(6):781-788.
[24] TU J,CHOI K K,PARK Y H. Design potential method for robust system parameter design[J]. American Institute of Aeronautics and Astronautics,2001,39(4):667-677.
[25] KAYMAZ I. Application of Kriging method to structural reliability problems[J]. Structural Safety,2005,27(2):133-151.
[26] LOPHAVEN S N,NIELSEN H B,SØNDERGAARD J.DACE-A Matlab Kriging toolbox[R]. Copenhagen:Technical University of Denmark,2002.
[27] Joint Committee on Structural Safety. JCSS probabilistic model code[S]. Copenhagen:JCSS Publication,2016.