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<p>The safety operation and maintenance of mega-structures in China are increasingly challenged by rare but high-impact structural failures. To address the difficulty in accurately estimating the low-probability tail of the response distribution, we propose a novel framework centered on the Tail-Sensitive Global Learning (TS-GL) algorithm. Unlike existing active learning-based Gaussian process (AL-GP) metamodels, TS-GL introduces a tail-focused search mechanism with a newly designed weight function, significantly improving the estimation of one-sided tail probabilities. To ensure computational practicality, the effect of different activation functions on iteration efficiency is also examined. The method is validated on a classical nonlinear system—the bond-slip relationship between steel and concrete—relevant to anchorage connections in subway tunnels. Insufficient anchorage length can cause excessive bolt slip and deformation, leading to gaps and leakage in underground structures. TS-GL outperforms AL-GP in both accuracy and efficiency when quantifying such rare events, providing a practical tool for uncertainty analysis in critical infrastructure.</p>

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Enhanced active learning Gaussian process metamodel for estimating the one-sided tail probability of nonlinear structural response

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月13日

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  • Yunzhe Wang,
  • Yanwen Huang,
  • Yihang Huang,
  • Weihao Yin

摘要

The safety operation and maintenance of mega-structures in China are increasingly challenged by rare but high-impact structural failures. To address the difficulty in accurately estimating the low-probability tail of the response distribution, we propose a novel framework centered on the Tail-Sensitive Global Learning (TS-GL) algorithm. Unlike existing active learning-based Gaussian process (AL-GP) metamodels, TS-GL introduces a tail-focused search mechanism with a newly designed weight function, significantly improving the estimation of one-sided tail probabilities. To ensure computational practicality, the effect of different activation functions on iteration efficiency is also examined. The method is validated on a classical nonlinear system—the bond-slip relationship between steel and concrete—relevant to anchorage connections in subway tunnels. Insufficient anchorage length can cause excessive bolt slip and deformation, leading to gaps and leakage in underground structures. TS-GL outperforms AL-GP in both accuracy and efficiency when quantifying such rare events, providing a practical tool for uncertainty analysis in critical infrastructure.