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Bayesian Approach for Sequential Probabilistic Back Analysis of Uncertain Geomechanical Parameters and Reliability Updating of Tunneling-Induced Ground Settlements

Author(s):



Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2020
Page(s): 1-13
DOI: 10.1155/2020/8528304
Abstract:

This paper proposes a new sequential probabilistic back analysis approach for probabilistically determining the uncertain geomechanical parameters of shield tunnels by using time-series monitoring data. The approach is proposed based on the recently developed Bayesian updating with subset simulation. Within the framework of the proposed approach, a complex Bayesian back analysis problem is transformed into an equivalent structural reliability problem based on subset simulation. Hermite polynomial chaos expansion-based surrogate models are constructed to improve the computational efficiency of probabilistic back analysis. The reliability of tunneling-induced ground settlements is updated in the process of sequential back analyses. A real shield tunnel project of No. 1 Nanchang Metro Line in China is investigated to assess the effectiveness of the approach. The proposed approach is able to infer the posterior distributions of uncertain geomechanical parameters (i.e., Young’s moduli of surrounding soil layers and ground vehicle load). The reliability of tunneling-induced ground settlements can be updated in a real-time manner by fully utilizing the time-series monitoring data. The results show good agreement with the variation trend of field monitoring data of ground settlement and the post-event investigations.

Copyright: © 2020 Cong Li et al.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10426549
  • Published on:
    13/07/2020
  • Last updated on:
    02/06/2021
 
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