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Optimal monitoring location for tracking evolving risks to infrastructure systems: Theory and application to tunneling excavation risk

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  • Wang, Zeyu
  • Shafieezadeh, Abdollah
  • Xiao, Xiong
  • Wang, Xiaowei
  • Li, Quanwang

Abstract

Structural health monitoring (SHM) technologies offer ever-increasing opportunities to continually observe various responses and states of structures, such as settlement-induced building damage. Recent advances in reliability updating have enabled estimating the probability of failing to meet a prescribed objective for systems using various types of information including those acquired from SHM. However, reliability updates are sensitive to monitoring location, especially when the risks are evolving. Therefore, there may exist optimal locations in a system for monitoring that yield maximum value for reliability updating. This paper proposes a computational framework for optimal monitoring location based on an innovative metric called sensitivity of information (SOI). This metric quantifies the change in unconditional and conditional reliability indexes, which subsequently facilitates fast exploration of optimal monitoring location by parameterizing an optimization function. A state-of-the-practice case related to assessing evolving risks posed by tunneling-induced settlement to buildings is explored in-depth with respect to the progression of tunneling. Simulation results showcase that the proposed framework can successfully find the monitoring location that is the most impactful to the accuracy of the updated reliability.

Suggested Citation

  • Wang, Zeyu & Shafieezadeh, Abdollah & Xiao, Xiong & Wang, Xiaowei & Li, Quanwang, 2022. "Optimal monitoring location for tracking evolving risks to infrastructure systems: Theory and application to tunneling excavation risk," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022004045
    DOI: 10.1016/j.ress.2022.108781
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    References listed on IDEAS

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    2. Zhenliang Yu & Zhili Sun & Shengnan Zhang & Jian Wang, 2022. "The Coupled Thermal-Structural Resonance Reliability Sensitivity Analysis of Gear-Rotor System with Random Parameters," Sustainability, MDPI, vol. 15(1), pages 1-16, December.
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    4. Wang, Zeyu & Shafieezadeh, Abdollah, 2023. "Bayesian updating with adaptive, uncertainty-informed subset simulations: High-fidelity updating with multiple observations," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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