Published March 15, 2025 | Published
Journal Article

Adaptive meta-learning stochastic gradient Hamiltonian Monte Carlo simulation for Bayesian updating of structural dynamic models

  • 1. ROR icon Harbin Institute of Technology
  • 2. ROR icon California Institute of Technology

Abstract

In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the field of structural health monitoring. Recently, several MCMC algorithms have been developed that incorporate neural networks to enhance their performance for specific Bayesian model updating problems. However, a common challenge with these approaches lies in the fact that the embedded neural networks often necessitate retraining when faced with new tasks, a process that is time-consuming and significantly undermines the competitiveness of these methods. This paper introduces a newly developed adaptive meta-learning stochastic gradient Hamiltonian Monte Carlo (AM-SGHMC) algorithm. The idea behind AM-SGHMC is to optimize the sampling strategy by training adaptive neural networks, and due to the adaptive design of the network inputs and outputs, the trained sampler can be directly applied to various Bayesian updating problems of the same type of structure without further training, thereby achieving meta-learning. Additionally, practical issues for the feasibility of the AM-SGHMC algorithm for structural dynamic model updating are addressed, and two examples involving Bayesian updating of multi-story building models with different model fidelity are used to demonstrate the effectiveness and generalization ability of the proposed method.

Copyright and License

© 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Acknowledgement

The study was supported by National Natural Science Foundation of China under Grant No. 52078174.

Contributions

Xianghao Meng: Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. James L. Beck: Writing – review & editing, Validation, Methodology. Yong Huang: Writing – review & editing, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Data curation, Conceptualization. Hui Li: Supervision, Conceptualization.

Data Availability

Data will be made available on request.

Additional details

Created:
February 6, 2025
Modified:
February 6, 2025