Bayesian Tendon Breakage Localization under Model Uncertainty Using Distributed Fiber Optic Sensors

2026-04-09

AI Summary

This study addresses the challenge of accurately locating tendon ruptures in prestressed concrete structures by proposing a novel methodology that integrates high-resolution strain measurements from distributed fiber-optic sensing with a Bayesian probabilistic framework. The approach explicitly incorporates model-form uncertainty through material parameters and leverages a Gaussian process surrogate model coupled with finite element simulations and Bayesian inference to jointly estimate physical parameters and quantify uncertainties. To enhance interpretability and robustness in damage identification, φ-divergence influence analysis and separability assessment are introduced. Validated on full-scale structural tests, the method demonstrates high-fidelity parameter calibration and reliably predicts the locations of tendon ruptures at varying depths.

Abstract

This study develops a Bayesian, uncertainty-aware framework for tendon breakage localization in pre-stressed concrete members using high-resolution data from distributed fiber-optic sensors (DFOS). DFOS enable full-field monitoring of strain changes on the surface of pre-stressed concrete members due to such failure. A finite element model (FEM) of an experimental tendon-breakage test is constructed, and model parameters are calibrated probabilistically against DFOS measurements. To capture model-form uncertainty (MFU), stochastic perturbations are embedded directly into material parameters, enabling the joint inference of physical properties and MFU within a unified probabilistic framework. Gaussian Process surrogates are employed to efficiently emulate the nonlinear FEM response, supporting computationally tractable Bayesian inference. A $φ$-divergence-based influence analysis identifies the DFOS measurements that most strongly shape the posterior distributions, providing interpretable diagnostics of sensor informativeness and model adequacy. The calibrated parameters and embedded uncertainties are then transferred to a FEM of a full-scale structural configuration, enabling prediction of tendon breakage localization under realistic conditions. A separability analysis of the predictive strain distributions quantifies the identifiability of tendon breakage at varying depths, assessing the confidence with which different damage scenarios can be distinguished given the propagated uncertainties. Results demonstrate that the framework achieves robust parameter calibration, interpretable diagnostics, and uncertainty-informed damage detection, integrating experimental data, embedded MFU, and probabilistic modeling. By systematically propagating both experimental and model uncertainties, the approach supports reliable tendon breakage localization and optimal DFOS placement.

Problem

• tendon breakage localization
• model uncertainty
• distributed fiber optic sensors
• pre-stressed concrete
• Bayesian inference

Innovation

• Bayesian inference
• model-form uncertainty
• distributed fiber-optic sensors
• Gaussian Process surrogate
• tendon breakage localization