IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v248y2024ics0951832024002497.html
   My bibliography  Save this article

Dimension-Reduction Spectral Representation of Soil Spatial Variability and Its Application in the Efficient Reliability Analysis of Seismic Response in Tunnels

Author

Listed:
  • Wu, Yongxin
  • Wang, Juncheng
  • Cheng, Jialiang
  • Yang, Shangchuan

Abstract

Reliability analysis of tunnels considering soil spatial variability was traditionally carried out using Monte Carlo simulations, which are computationally expensive. This study proposes a dimension-reduction spectral representation method (SRM) for simulating parameter stochastic fields to capture uncertainties in soil properties, which can be coupled with the probability density evolution method (PDEM). Additionally, the PDEM is employed to conduct seismic reliability assessments of tunnels in a more efficient manner. By integrating a non-intrusive dynamic random finite element method (FEM), this study explores the horizontal drift angle and vertical relative settlement of tunnel dynamic response under different coefficient of variations (COVs) and horizontal autocorrelation lengths of elastic modulus (E). Results show the changes in COVs have more pronounced effects on the mean and standard deviation of horizontal drift angle and vertical relative settlement of tunnel than alterations in horizontal autocorrelation lengths. A limit state pertaining to the horizontal drift angle and vertical relative settlement is established through the use of a probability density function (PDF) and cumulative distribution function (CDF). The results suggest that the combination of soil spatial variability and PDEM can serve as an effective approach for practical tunnel design.

Suggested Citation

  • Wu, Yongxin & Wang, Juncheng & Cheng, Jialiang & Yang, Shangchuan, 2024. "Dimension-Reduction Spectral Representation of Soil Spatial Variability and Its Application in the Efficient Reliability Analysis of Seismic Response in Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:reensy:v:248:y:2024:i:c:s0951832024002497
    DOI: 10.1016/j.ress.2024.110175
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024002497
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110175?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. Ding, Jiayi & Zhou, Jianfang & Cai, Wei, 2023. "An efficient variable selection-based Kriging model method for the reliability analysis of slopes with spatially variable soils," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Pang, Rui & Zai, Dezhi & Xu, Bin & Liu, Jun & Zhao, Chunfeng & Fan, Qunying & Chen, Yuting, 2023. "Stochastic dynamic and reliability analysis of AP1000 nuclear power plants via DPIM subjected to mainshock-aftershock sequences," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. 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).
    4. Heo, Yunyeong & Lee, Seung Jun, 2021. "Development of a multi-unit seismic conditional core damage probability model with uncertainty analysis," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Jerez, Danko J. & Chwała, M. & Jensen, Hector A. & Beer, Michael, 2024. "Optimal borehole placement for the design of rectangular shallow foundation systems under undrained soil conditions: A stochastic framework," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Do, Duy Minh & Gao, Wei & Song, Chongmin & Tangaramvong, Sawekchai, 2014. "Dynamic analysis and reliability assessment of structures with uncertain-but-bounded parameters under stochastic process excitations," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 46-59.
    7. 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).
    8. 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).
    9. Pei, Pei & Zhou, Tong, 2023. "One-step look-ahead policy for active learning reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 236(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. Zhou, Tong & Guo, Tong & Dong, You & Yang, Fan & Frangopol, Dan M., 2024. "Look-ahead active learning reliability analysis based on stepwise margin reduction," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. 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).
    3. Nguyen, Phong T.T. & Manuel, Lance, 2024. "Uncertainty quantification in low-probability response estimation using sliced inverse regression and polynomial chaos expansion," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    4. Xu, Zidong & Wang, Hao & Zhao, Kaiyong & Zhang, Han & Liu, Yun & Lin, Yuxuan, 2024. "Evolutionary probability density reconstruction of stochastic dynamic responses based on physics-aided deep learning," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    5. Pang, Rui & Zai, Dezhi & Xu, Bin & Liu, Jun & Zhao, Chunfeng & Fan, Qunying & Chen, Yuting, 2023. "Stochastic dynamic and reliability analysis of AP1000 nuclear power plants via DPIM subjected to mainshock-aftershock sequences," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. 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).
    7. 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).
    8. Das, Sourav & Tesfamariam, Solomon, 2024. "Reliability assessment of stochastic dynamical systems using physics informed neural network based PDEM," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Wang, Jiaqi & Zhang, Limao & Yang, Hui & Liu, Huabei & Skibniewski, Mirosław J., 2024. "Dynamic reliability analysis of Aerial Building Machine under extreme wind loads using improved QBDC-based active learning," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    10. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    11. Jiadong, Qiu & Ohl, Joy P. & Tran, Trung-Tin, 2024. "Predicting clay compressibility for foundation design with high reliability and safety: A geotechnical engineering perspective using artificial neural network and five metaheuristic algorithms," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    12. Hao, Peng & Tang, Hao & Wang, Yu & Wu, Tao & Feng, Shaojun & Wang, Bo, 2023. "Stochastic isogeometric buckling analysis of composite shell considering multiple uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    13. 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).
    14. Zheng, Zhi & Tian, Aonan & Pan, Xiaolan & Ji, Duofa & Wang, Yong, 2024. "The damage-based fragility analysis and probabilistic safety assessment of containment under internal pressure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. DeJesus Segarra, Jonathan & Bensi, Michelle & Modarres, Mohammad, 2023. "Multi-unit seismic probabilistic risk assessment: A Bayesian network perspective," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    16. 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).
    17. 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).
    18. Jerez, Danko J. & Chwała, M. & Jensen, Hector A. & Beer, Michael, 2024. "Optimal borehole placement for the design of rectangular shallow foundation systems under undrained soil conditions: A stochastic framework," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    19. Liu, Jie & Hu, Yifeng & Xu, Can & Jiang, Chao & Han, Xu, 2016. "Probability assessments of identified parameters for stochastic structures using point estimation method," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 51-58.
    20. Kim, Man Cheol, 2022. "Systematic approach and mathematical development for conditional core damage probabilities under station blackout of a nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 217(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:248:y:2024:i:c:s0951832024002497. 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.