Skip to main navigation Skip to search Skip to main content

Fragility Analysis Method for Circular Tunnels Based on Parametric Seismic Intensity Threshold Models

  • Jingzhou Zhu
  • , Longjun Xu
  • , Guochen Zhao
  • , Shuang Li*
  • *Corresponding author for this work
  • School of Civil Engineering, Harbin Institute of Technology
  • Jianghan University

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a novel approach for the seismic fragility analysis of circular tunnels, focusing on the development of parametric seismic intensity threshold models. To ensure the broad applicability of the parametric models to be constructed, the uncertainties in tunnel parameters (tunnel size, material, and depth) and site conditions have been thoroughly considered, resulting in the generation of 27 representative soil–tunnel configurations. Then, the optimal seismic intensity measure for seismic fragility analysis of circular tunnels is discussed. Subsequently, parametric seismic intensity threshold models for different damage states of circular tunnels have been developed based on the results of extensive numerical simulations. A case study demonstrates the practical application of our method, showing that it can rapidly and accurately estimate the seismic fragility of a specific tunnel based on given parameter values. This capability cannot only enable the rapid prediction of post-earthquake functionality for existing tunnels but also help optimize the seismic design of tunnels under construction, thereby effectively managing seismic risks.

Original languageEnglish
Article number2650377
JournalInternational Journal of Structural Stability and Dynamics
DOIs
StateAccepted/In press - 2025
Externally publishedYes

Keywords

  • Circular tunnel
  • fragility analysis
  • nonlinear dynamic analysis
  • parametric model
  • seismic intensity threshold

Fingerprint

Dive into the research topics of 'Fragility Analysis Method for Circular Tunnels Based on Parametric Seismic Intensity Threshold Models'. Together they form a unique fingerprint.

Cite this