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

TL;DR

This paper presents a Bayesian framework utilizing distributed fiber-optic sensors and probabilistic modeling to accurately localize tendon breakage in pre-stressed concrete, accounting for model and measurement uncertainties.

Contribution

It introduces a novel uncertainty-aware Bayesian approach with Gaussian Process surrogates for efficient, interpretable tendon damage localization under model form uncertainty.

Findings

Robust parameter calibration achieved with embedded uncertainties.

Influence analysis identifies most informative sensors.

Framework enables reliable damage detection with uncertainty quantification.

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 $\phi$-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.