Work

Bayesian structural model updating under prior misspecification using conditional normalizing flows

Year: 2026

Type: article

Abstract: This study proposes a likelihood-free Bayesian structural model updating framework that is robust under prior misspecification. The approach employs Conditional Normalizing Flows to learn posterior distributions directly from simulated structural responses. To mitigate the degradation caused by imperfect engineering priors, a proposal-based training strategy and prior-proposal correction are introduced to ensure consistent posterior inference. Benchmark and nonlinear structural case studies demonstrate that the method accurately recovers multimodal posteriors and enables efficient structural model updating using high-dimensional acceleration time-history data.

Source: Journal of Asian Architecture and Building Engineering

DOI: 10.1080/13467581.2026.2706262

Authors: Tatsuya Itoi, Toshiya Takeda, Sangwon Lee, Taro Yaoyama

Institution: The University of Tokyo

Language: English

Topic: Structural Health Monitoring Techniques

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,165

Funder: Japan Society for the Promotion of Science