IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v198y2020ics095183201931049x.html
   My bibliography  Save this article

Reliability analysis and design optimization of nonlinear structures

Author

Listed:
  • Ni, Pinghe
  • Li, Jun
  • Hao, Hong
  • Yan, Weimin
  • Du, Xiuli
  • Zhou, Hongyuan

Abstract

Reliability analysis and design optimization of structures have been gaining a significant amount of attention in recent decades. Most of the current studies are based on linear structural analysis. The study on reliability analysis and design optimization for nonlinear structures has not been well explored. This paper presents studies on reliability analysis and design optimization for nonlinear structures, by using the Kriging based method and First-order reliability method (FORM). Numerical studies on nonlinear reinforced concrete structures and steel frame structures are carried out to verify the accuracy and efficiency of the proposed methods. The results demonstrate that the FORM and Kriging based methods have the same accuracy as those from Monte Carlo Simulation (MCS) method. Reliability-based design optimization (RBDO) is conducted for nonlinear structures, in which the dimensions of structures can be optimized and the target occurrence probability can be achieved. Compared with FORM based RBDO method, the Kriging based method is more accurate and efficient. The response sensitivity is not required in the Kriging based method, which makes it more versatile.

Suggested Citation

  • Ni, Pinghe & Li, Jun & Hao, Hong & Yan, Weimin & Du, Xiuli & Zhou, Hongyuan, 2020. "Reliability analysis and design optimization of nonlinear structures," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
  • Handle: RePEc:eee:reensy:v:198:y:2020:i:c:s095183201931049x
    DOI: 10.1016/j.ress.2020.106860
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095183201931049X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2020.106860?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ling, Chunyan & Lu, Zhenzhou & Zhu, Xianming, 2019. "Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 23-35.
    2. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
    3. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    4. Qin, Quan & Lin, Daojin & Mei, Gang & Chen, Hao, 2006. "Effects of variable transformations on errors in FORM results," Reliability Engineering and System Safety, Elsevier, vol. 91(1), pages 112-118.
    5. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    6. Zhang, Xufang & Wang, Lei & Sørensen, John Dalsgaard, 2019. "REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 440-454.
    7. Sudret, Bruno, 2008. "Global sensitivity analysis using polynomial chaos expansions," Reliability Engineering and System Safety, Elsevier, vol. 93(7), pages 964-979.
    8. Cadini, F. & Santos, F. & Zio, E., 2014. "An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 109-117.
    9. Wang, Zeyu & Shafieezadeh, Abdollah, 2019. "REAK: Reliability analysis through Error rate-based Adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 33-45.
    10. Chen, Hua-Peng & Mehrabani, Mehrdad Bahari, 2019. "Reliability analysis and optimum maintenance of coastal flood defences using probabilistic deterioration modelling," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 163-174.
    11. Izquierdo, J. & Crespo Márquez, A. & Uribetxebarria, J., 2019. "Dynamic artificial neural network-based reliability considering operational context of assets," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 483-493.
    12. Yuan, Kai & Xiao, Ning-Cong & Wang, Zhonglai & Shang, Kun, 2020. "System reliability analysis by combining structure function and active learning kriging model," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    13. Dong, Y. & Teixeira, A.P. & Guedes Soares, C., 2020. "Application of adaptive surrogate models in time-variant fatigue reliability assessment of welded joints with surface cracks," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    14. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    15. Alibrandi, Umberto, 2014. "A response surface method for stochastic dynamic analysis," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 44-53.
    16. Menz, Morgane & Gogu, Christian & Dubreuil, Sylvain & Bartoli, Nathalie & Morio, Jérôme, 2020. "Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    17. Papaioannou, Iason & Geyer, Sebastian & Straub, Daniel, 2019. "Improved cross entropy-based importance sampling with a flexible mixture model," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Yiming & Peng, Jianxin & Cai, C.S. & Zhou, Yadong & Wang, Lei & Zhang, Jianren, 2022. "Time-dependent reliability assessment of aging structures considering stochastic resistance degradation process," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    2. Qi, Yaqun & Jin, Ping & Cai, Guobiao & Li, Ruizhi, 2022. "A Bi-stage Multi-objective Reliability-based Design Optimization Using Surrogate Model for Reusable Thrust Chambers," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Li, Xiaoke & Zhu, Heng & Chen, Zhenzhong & Ming, Wuyi & Cao, Yang & He, Wenbin & Ma, Jun, 2022. "Limit state Kriging modeling for reliability-based design optimization through classification uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    4. Li Lu & Yizhong Wu & Qi Zhang & Ping Qiao, 2023. "A Transformation-Based Improved Kriging Method for the Black Box Problem in Reliability-Based Design Optimization," Mathematics, MDPI, vol. 11(1), pages 1-19, January.
    5. Alibeikloo, Mehrnaz & Khabbaz, Hadi & Fatahi, Behzad, 2022. "Random Field Reliability Analysis for Time-Dependent Behaviour of Soft Soils Considering Spatial Variability of Elastic Visco-Plastic Parameters," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Van Huynh, Thu & Tangaramvong, Sawekchai & Do, Bach & Gao, Wei & Limkatanyu, Suchart, 2023. "Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Abdollahi, Azam & Amini, Ali & Hariri-Ardebili, Mohammad Amin, 2022. "An uncertainty-aware dynamic shape optimization framework: Gravity dam design," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    8. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Yaqun, Qi & Ping, Jin & Ruizhi, Li & Sheng, Zhang & Guobiao, Cai, 2020. "Dynamic reliability analysis for the reusable thrust chamber: A multi-failure modes investigation based on coupled thermal-structural analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    10. Okoro, Aghatise & Khan, Faisal & Ahmed, Salim, 2023. "Dependency effect on the reliability-based design optimization of complex offshore structure," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Alibeikloo, Mehrnaz & Khabbaz, Hadi & Fatahi, Behzad & Le, Thu Minh, 2021. "Reliability Assessment for Time-Dependent Behaviour of Soft Soils Considering Cross Correlation between Visco-Plastic Model Parameters," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    12. Hongyan Dui & Huiting Xu & Yun-An Zhang, 2022. "Reliability Analysis and Redundancy Optimization of a Command Post Phased-Mission System," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    13. Shirgir, Sina & Shamsaddinlou, Amir & Zare, Reza Najafi & Zehtabiyan, Sorour & Bonab, Masoud Hajialilue, 2023. "An efficient double-loop reliability-based optimization with metaheuristic algorithms to design soil nail walls under uncertain condition," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Bakeer, Tammam, 2023. "General partial safety factor theory for the assessment of the reliability of nonlinear structural systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    15. Jeongbae Jeon & Hyeongryeol Lee & Seongsoo Yoon, 2022. "Optimal Section Design of Korean Agricultural Greenhouse Response to Climate Change Based on Monte Carlo Simulation," Agriculture, MDPI, vol. 12(9), pages 1-15, September.
    16. Rivier, M. & Congedo, P.M., 2022. "Surrogate-Assisted Bounding-Box approach applied to constrained multi-objective optimisation under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Chen & Qiu, Haobo & Gao, Liang & Wang, Dapeng & Yang, Zan & Chen, Liming, 2020. "EEK-SYS: System reliability analysis through estimation error-guided adaptive Kriging approximation of multiple limit state surfaces," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    2. Shi, Yan & Lu, Zhenzhou & He, Ruyang & Zhou, Yicheng & Chen, Siyu, 2020. "A novel learning function based on Kriging for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Zhang, Jinhao & Gao, Liang & Xiao, Mi, 2020. "A composite-projection-outline-based approximation method for system reliability analysis with hybrid uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    4. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    5. Wang, Jian & Sun, Zhili & Cao, Runan, 2021. "An efficient and robust Kriging-based method for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    6. Saraygord Afshari, Sajad & Enayatollahi, Fatemeh & Xu, Xiangyang & Liang, Xihui, 2022. "Machine learning-based methods in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Yang, Seonghyeok & Lee, Mingyu & Lee, Ikjin, 2023. "A new sampling approach for system reliability-based design optimization under multiple simulation models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Teixeira, Rui & Martinez-Pastor, Beatriz & Nogal, Maria & O’Connor, Alan, 2021. "Reliability analysis using a multi-metamodel complement-basis approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    9. Xiao, Mi & Zhang, Jinhao & Gao, Liang, 2020. "A system active learning Kriging method for system reliability-based design optimization with a multiple response model," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    10. Wang, Zeyu & Shafieezadeh, Abdollah, 2020. "Real-time high-fidelity reliability updating with equality information using adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    11. Zhang, Yu & Dong, You & Xu, Jun, 2023. "An accelerated active learning Kriging model with the distance-based subdomain and a new stopping criterion for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    12. Li, Peiping & Wang, Yu, 2022. "An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS)," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    13. Wang, Jinsheng & Xu, Guoji & Li, Yongle & Kareem, Ahsan, 2022. "AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    14. Chen, Weidong & Xu, Chunlong & Shi, Yaqin & Ma, Jingxin & Lu, Shengzhuo, 2019. "A hybrid Kriging-based reliability method for small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 31-41.
    15. Yang, Seonghyeok & Jo, Hwisang & Lee, Kyungeun & Lee, Ikjin, 2022. "Expected system improvement (ESI): A new learning function for system reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    16. Xu, Jun & Wang, Ding, 2019. "Structural reliability analysis based on polynomial chaos, Voronoi cells and dimension reduction technique," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 329-340.
    17. Menz, Morgane & Gogu, Christian & Dubreuil, Sylvain & Bartoli, Nathalie & Morio, Jérôme, 2020. "Adaptive coupling of reduced basis modeling and Kriging based active learning methods for reliability analyses," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    18. Li, Junxiang & Chen, Jianqiao, 2019. "Solving time-variant reliability-based design optimization by PSO-t-IRS: A methodology incorporating a particle swarm optimization algorithm and an enhanced instantaneous response surface," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    19. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    20. Keshtegar, Behrooz & Chakraborty, Subrata, 2018. "An efficient-robust structural reliability method by adaptive finite-step length based on Armijo line search," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 195-206.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:198:y:2020:i:c:s095183201931049x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.