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; 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.

该提案的目的是开发基于仿真模型来支持数字双桥创建数字双胞胎的方法。特别是，这应该包括对模拟结果预后质量的估计。该估计值是通过使用实验室和监视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，设计的建模假设与实际实施不同。因此，必须进行自适应过程，其中添加或更改了其他物理效应（边界条件，增强层的几何形状，本构定律），直到数字和实际双胞胎在规定的公差内具有相同的属性。但是，另一方面，由于数据空间的高维度（大数量和不同类型的传感器），不能手动进行比较，另一方面也无法手动进行，另一方面也是由于监测导致的大量数据。此外，由于基础微分方程的椭圆度，模型中一个点的局部变化会导致全局行为的变化，即通常不可能从数据中定位模型偏差。 因此，该项目的目的是开发一种半自动方法。基于最初的，物理动机的模型，使用数据校准了模型的参数。另外，从数据中确定了可能的改进。将根据高斯过程，材料参数的随机字段或稀疏的词典学习方法的不同方法进行比较。基于此数据驱动的模型偏差，建模者应开发出具有物理动机的模型改进（可能具有其他参数），直到模拟模型足够准确。 需要大量的向前模型计算（桥的Fe模型）来校准模型参数。为了提高方法的性能，向前模型被元模型所取代。特别是，用于此目的的高斯过程和贝叶斯物理知识的神经网络用于此目的。 该方法将在项目中的不同示例中以不同程度的复杂性进行验证，从简单的梁模型（包括故障）开始，我们的测试站点上的现有桥梁演示器，最后是来自SPP示威者的真实数据。与实施方法的实施相似，旨在将接口的界面连续开发到SPP中的其他项目，并将集成到通用平台中。