Data driven model adaptation for identifying stochastic digital twins of bridges

用于识别桥梁随机数字孪生的数据驱动模型适应

• 批准号: 501811638
• 负责人: Dr.-Ing. Jörg F. Unger
• 金额: --
• 依托单位: Zuse-Institut Berlin (ZIB)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501811638?language=en
• 关键词: Data; driven; model; adaptation; identifying

The goal of the proposal is the development of methods to support the creation of digital twins of bridges based on simulation models. In particular, this should include an estimate of the quality of prognosis of simulation results. This estimate is obtained via Bayes inference procedures using both laboratory as well as monitoring data. A particular challenge in the development of a simulation model for bridges is that in many cases the modelling assumptions from the design differ from the real implementation. For this reason, an adaptive process is necessary in which additional physical effects are added or changed (boundary conditions, geometry of the reinforcement layers, constitutive laws) until the digital and the real twin have identical properties within prescribed tolerances. However, the comparison between measured data and simulation results cannot be done manually, on the one hand due to the high dimension of the data space (large number and different types of sensors), on the other hand also due to the large amount of data resulting from monitoring. Furthermore, due to the ellipticity of the underlying differential equations, local changes at one point in the model lead to changes of the global behavior, i.e. the localization of model bias only from the data is often not possible. Therefore, the goal of the project is to develop a semi-automatic approach. Based on an initial, physically motivated model, the parameters of the model are calibrated using the data. In addition, a possible model improvement is identified from the data. Different approaches based on Gaussian processes, random fields for material parameters or a sparse dictionary learning approaches will be compared. Based on this data-driven model bias, the modeler shall develop a physically motivated model improvement (possibly with additional parameters) until the simulation model is sufficiently accurate. A large number of calculations of the forward model (FE model of the bridge) is required to calibrate the model parameters. In order to improve the performance of the methodology, the forward models are replaced by metamodels. In particular, Gaussian processes and Bayesian physics-informed neural networks are used for this purpose. The methodology will be validated within the project on different examples with varying degrees of complexity, starting with virtual data with simple beam models (including faults), existing bridge demonstrators on our test site and, finally, with real data from the demonstrator in the SPP. Parallel to the implementation of the methodology, a continuous development of the interfaces to the other projects in the SPP and an integration into a common platform is intended.

该提案的目标是开发方法，以支持基于仿真模型的桥梁数字双胞胎的创建。特别是，这应该包括对模拟结果预测质量的估计。这一估计是通过使用实验室和监测数据的贝叶斯推理程序获得的。开发桥梁模拟模型的一个特殊挑战是，在许多情况下，设计中的建模假设与实际实现不同。出于这个原因，需要一个自适应过程，在这个过程中添加或改变额外的物理效应（边界条件、增强层的几何形状、本构定律），直到数字孪生体和真实孪生体在规定的公差范围内具有相同的性能。然而，由于数据空间的高维（传感器数量多、种类多），以及由于监测产生的数据量大，测量数据与仿真结果的比较无法手工完成。此外，由于底层微分方程的椭圆性，模型中某一点的局部变化会导致全局行为的变化，即仅从数据中定位模型偏差往往是不可能的。因此，该项目的目标是开发一种半自动方法。基于初始的、物理驱动的模型，使用数据校准模型的参数。此外，从数据中确定了可能的模型改进。将比较基于高斯过程、材料参数随机场或稀疏字典学习方法的不同方法。基于这种数据驱动的模型偏差，建模者应开发物理驱动的模型改进（可能使用额外的参数），直到仿真模型足够准确。为了校正模型参数，需要进行大量的正演模型（桥梁有限元模型）计算。为了提高方法的性能，将前向模型替换为元模型。特别地，高斯过程和贝叶斯物理信息神经网络被用于此目的。该方法将在项目中对不同复杂程度的不同示例进行验证，从简单梁模型（包括故障）的虚拟数据开始，从我们测试现场的现有桥梁演示器开始，最后是SPP中演示器的真实数据。在实施该方法的同时，SPP中其他项目的接口将不断开发，并集成到一个公共平台中。