Simulation-based digital twins must provide accurate, robust and reliable digital representations of their physical counterparts. Quantifying the uncertainty in their predictions plays, therefore, a key role in making better-informed decisions that impact the actual system. The update of the simulation model based on data must be then carefully implemented. When applied to complex standing structures such as bridges, discrepancies between the computational model and the real system appear as model bias, which hinders the trustworthiness of the digital twin and increases its uncertainty. Classical Bayesian updating approaches aiming to infer the model parameters often fail at compensating for such model bias, leading to overconfident and unreliable predictions. In this paper, two alternative model bias identification approaches are evaluated in the context of their applicability to digital twins of bridges. A modularized version of Kennedy and O'Hagan's approach and another one based on Orthogonal Gaussian Processes are compared with the classical Bayesian inference framework in a set of representative benchmarks. Additionally, two novel extensions are proposed for such models: the inclusion of noise-aware kernels and the introduction of additional variables not present in the computational model through the bias term. The integration of such approaches in the digital twin corrects the predictions, quantifies their uncertainty, estimates noise from unknown physical sources of error and provides further insight into the system by including additional pre-existing information without modifying the computational model.

翻译：基于仿真的数字孪生体必须为其物理对应对象提供准确、稳健且可靠的数字表征。因此，量化其预测中的不确定性对于做出影响实际系统的最佳决策起着关键作用。基于数据对仿真模型的更新必须谨慎实施。当应用于桥梁等复杂在役结构时，计算模型与真实系统之间的差异表现为模型偏差，这削弱了数字孪生体的可信度并增加了其不确定性。旨在推断模型参数的传统贝叶斯更新方法往往无法补偿这种模型偏差，导致过度自信且不可靠的预测。本文针对桥梁数字孪生体的应用场景，评估了两种替代性模型偏差识别方法。将Kennedy与O'Hagan方法的模块化版本以及基于正交高斯过程的另一种方法，与经典贝叶斯推断框架在一组代表性基准测试中进行了比较。此外，针对此类模型提出了两项新扩展：引入噪声感知核函数，以及通过偏差项引入计算模型中未包含的额外变量。将这些方法整合至数字孪生体后，可校正预测值、量化其不确定性、估计未知物理误差源引入的噪声，并能在不修改计算模型的前提下，通过纳入额外的先验信息提供对系统的更深入洞察。