IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v235y2023ics0951832023001370.html
   My bibliography  Save this article

Reliability analysis of structures with inerter-based isolation layer under stochastic seismic excitations

Author

Listed:
  • Li, Jin-Yang
  • Lu, Jubin
  • Zhou, Hao

Abstract

Reliability analyses on various control devices are of vital importance in examining and quantifying their practical feasibility and effectiveness. In specific, an emerging base isolation technique using a tuned inerter damper (TID) draws researchers’ attention given some of its unique merits, such as the compact damper size, lightweight, and the fact that it will not compromise structural stiffness when the system is static and thus prevents the long-term creeping issue. To justify its feasibility and robustness in face of random earthquake excitations, this paper presents both (1) a simulation-based stochastic dynamic response analysis, and (2) a reliability analysis of a benchmark reinforced concrete (RC) structure with a TID isolation layer. It is found that although seismic randomness shall substantially affect the structural dynamic responses, the introduction of a TID isolation layer to the structure will significantly alleviate this effect and subsequently enhances the overall system robustness, which can be parallelly identified from the notable different features between their corresponding probability density evolutions. In addition, the reliability of the RC structure is also drastically improved owing to the introduction of the TID isolation layer, which highlights its effectiveness and sheds light on its great potential in future industrial applications.

Suggested Citation

  • Li, Jin-Yang & Lu, Jubin & Zhou, Hao, 2023. "Reliability analysis of structures with inerter-based isolation layer under stochastic seismic excitations," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  • Handle: RePEc:eee:reensy:v:235:y:2023:i:c:s0951832023001370
    DOI: 10.1016/j.ress.2023.109222
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023001370
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ress.2023.109222?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. Liu, Gang & Gao, Kai & Yang, Qingshan & Tang, Wei & Law, S.S., 2021. "Improvement to the discretized initial condition of the generalized density evolution equation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Peng, Yongbo & Ma, Yangying & Huang, Tianchen & De Domenico, Dario, 2021. "Reliability-based design optimization of adaptive sliding base isolation system for improving seismic performance of structures," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Maidana, Renan G. & Parhizkar, Tarannom & Gomola, Alojz & Utne, Ingrid B. & Mosleh, Ali, 2023. "Supervised dynamic probabilistic risk assessment: Review and comparison of methods," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    4. Zhou, Tong & Peng, Yongbo, 2022. "Ensemble of metamodels-assisted probability density evolution method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. Zhou, Jin & Li, Jie, 2022. "An enhanced method for improving the accuracy of small failure probability of structures," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Jensen, H.A. & Muñoz, A. & Papadimitriou, C. & Millas, E., 2016. "Model-reduction techniques for reliability-based design problems of complex structural systems," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 204-217.
    7. Zhang, Yang & Xu, Jun & Beer, Michael, 2023. "A single-loop time-variant reliability evaluation via a decoupling strategy and probability distribution reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    8. Zhou, Jin & Li, Jie, 2023. "IE-AK: A novel adaptive sampling strategy based on information entropy for Kriging in metamodel-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. Zhou, Tong & Peng, Yongbo, 2022. "Reliability analysis using adaptive Polynomial-Chaos Kriging and probability density evolution method," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    10. Zhang, Qiang & Zhao, Yan-Gang & Kolozvari, Kristijan & Xu, Lei, 2022. "Reliability analysis of reinforced concrete structure against progressive collapse," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Xu, Jun & Song, Jinheng & Yu, Quanfu & Kong, Fan, 2023. "Generalized distribution reconstruction based on the inversion of characteristic function curve for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    12. He, Jingran & Gao, Ruofan & Chen, Jianbing, 2022. "A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Pei, Pei & Zhou, Tong, 2023. "One-step look-ahead policy for active learning reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    3. Dhulipala, Somayajulu L.N. & Shields, Michael D. & Chakroborty, Promit & Jiang, Wen & Spencer, Benjamin W. & Hales, Jason D. & Labouré, Vincent M. & Prince, Zachary M. & Bolisetti, Chandrakanth & Che, 2022. "Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Luo, Changqi & Zhu, Shun-Peng & Keshtegar, Behrooz & Niu, Xiaopeng & Taylan, Osman, 2023. "An enhanced uniform simulation approach coupled with SVR for efficient structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    5. 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).
    6. Zheng, Xiaohu & Yao, Wen & Zhang, Yunyang & Zhang, Xiaoya, 2022. "Consistency regularization-based deep polynomial chaos neural network method for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    7. Zhou, Tong & Peng, Yongbo, 2022. "Ensemble of metamodels-assisted probability density evolution method for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    8. Liu, Gang & Gao, Kai & Yang, Qingshan & Tang, Wei & Law, S.S., 2021. "Improvement to the discretized initial condition of the generalized density evolution equation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. 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).
    10. Wang, Yangpeng & Li, Shuxiang & Lee, Kangkuen & Tam, Hwayaw & Qu, Yuanju & Huang, Jingyin & Chu, Xianghua, 2023. "Accident risk tensor-specific covariant model for railway accident risk assessment and prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    11. Lilli, Giordano & Sanavia, Matteo & Oboe, Roberto & Vianello, Chiara & Manzolaro, Mattia & De Ruvo, Pasquale Luca & Andrighetto, Alberto, 2024. "A semi-quantitative risk assessment of remote handling operations on the SPES Front-End based on HAZOP-LOPA," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    12. Li, Jian & Dueñas-Osorio, Leonardo & Chen, Changkun & Shi, Congling, 2016. "Connectivity reliability and topological controllability of infrastructure networks: A comparative assessment," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 24-33.
    13. Millar, Robert & Li, Hui & Li, Jinglai, 2023. "Multicanonical sequential Monte Carlo sampler for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Mathpati, Yogesh Chandrakant & More, Kalpesh Sanjay & Tripura, Tapas & Nayek, Rajdip & Chakraborty, Souvik, 2023. "MAntRA: A framework for model agnostic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    15. Kröker, Ilja & Oladyshkin, Sergey, 2022. "Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    16. Li, Guofa & Wang, Tianzhe & Chen, Zequan & He, Jialong & Wang, Xiaoye & Du, Xuejiao, 2023. "RBIK-SS: A parallel adaptive structural reliability analysis method for rare failure events," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    17. Li, Guosheng & Ma, Shuaichao & Zhang, Dequan & Yang, Leping & Zhang, Weihua & Wu, Zeping, 2024. "An efficient sequential anisotropic RBF reliability analysis method with fast cross-validation and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    18. Å nipas, Mindaugas & Radziukynas, Virginijus & ValakeviÄ ius, Eimutis, 2018. "Numerical solution of reliability models described by stochastic automata networks," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 570-578.
    19. Yoon, Joung Taek & Youn, Byeng D. & Yoo, Minji & Kim, Yunhan, 2017. "A newly formulated resilience measure that considers false alarms," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 417-427.
    20. Pepper, Nick & Crespo, Luis & Montomoli, Francesco, 2022. "Adaptive learning for reliability analysis using Support Vector Machines," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

    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:235:y:2023:i:c:s0951832023001370. 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.