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.

翻译：本研究发展了一种贝叶斯不确定性感知框架，利用分布式光纤传感器（DFOS）的高分辨率数据实现预应力混凝土构件中筋腱断裂的定位。DFOS能够对因筋腱断裂引起的预应力混凝土构件表面应变变化进行全场监测。本研究构建了实验筋腱断裂试验的有限元模型（FEM），并以概率方式依据DFOS测量数据校准模型参数。为捕捉模型形式不确定性（MFU），将随机扰动直接嵌入材料参数中，从而在统一概率框架内实现物理特性与MFU的联合推断。采用高斯过程替代模型高效模拟非线性FEM响应，支持计算可行的贝叶斯推断。基于φ散度的敏感性分析识别出对后验分布影响最显著的DFOS测量数据，为传感器信息量和模型充分性提供可解释的诊断依据。将校准后的参数及嵌入的不确定性迁移至全尺寸结构构件的FEM中，实现现实条件下筋腱断裂定位的预测。通过预测应变分布的可分离性分析，量化不同深度筋腱断裂的可识别性，评估在传播不确定性条件下区分不同损伤场景的置信度。结果表明，该框架集成了实验数据、嵌入型MFU与概率建模，实现了稳健的参数校准、可解释的诊断分析以及考虑不确定性的损伤检测。通过系统传播实验与模型两类不确定性，该方法可支撑可靠的筋腱断裂定位及DFOS优化布设。