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首页> 外文期刊>Engineering Structures >Bayesian updating of complex nonlinear FE models with high-dimensional parameter space using heterogeneous measurements and a batch-recursive approach
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Bayesian updating of complex nonlinear FE models with high-dimensional parameter space using heterogeneous measurements and a batch-recursive approach

机译：使用异类测量和批处理递归方法对具有高维参数空间的复杂非线性有限元模型进行贝叶斯更新

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摘要

Finite element (FE) model updating aims to minimize the discrepancy between measured and FE-predicted responses of instrumented structural systems. In the last decades, significant efforts have focused on linear FE models, including recent studies investigating applications with large models (i.e., models with many degrees-of-freedom) and/or models with a large number of parameters to be estimated (i.e., high-dimensional parameter space). Recently, increasing interests have been attracted to the calibration of nonlinear FE models, which has emerged as an attractive approach for damage diagnosis and prognosis, chiefly if Bayesian methods are employed to solve the inverse parameter estimation problem. A crucial step towards the application of damage identification methods based on nonlinear FE model updating in the real-world, is the validation for cases involving large and complex nonlinear FE models requiring the estimation of a high number of parameters. In this paper, the performance of the unscented Kalman filter (UKF) in updating these types of models is investigated and a batch-recursive variant to reduce the computational cost is proposed. In addition, the effects of considering heterogeneous response measurements are studied. Two application examples of large and complex FE models involving strong nonlinearities, including a two-dimensional steel frame building and a three-dimensional isolated bridge, with high number of unknown model parameters are examined. Significant computational time savings of the presented batch-recursive approach, without sacrificing the estimation performance, are found. This confirms the feasibility of using Bayesian techniques to calibrate large and complex hysteretic FE models of real-world systems with high-dimensional parameter space. The successful results obtained here show that the presented approach represents a novel and promising tool to update large nonlinear structural FE models involving a great number of parameters whose calibration might become prohibitive by means of conventional updating techniques.
机译：有限元（FE）模型更新旨在最大程度地减少仪器结构系统的测量响应与有限元预测响应之间的差异。在过去的几十年中，大量的精力集中在线性有限元模型上，包括最近的调查研究了大型模型（即具有许多自由度的模型）和/或具有大量待估计参数的模型（即，高维参数空间）。近年来，非线性有限元模型的校准引起了越来越多的兴趣，这已经成为一种用于损伤诊断和预后的有吸引力的方法，主要是采用贝叶斯方法来解决逆参数估计问题。在现实世界中，应用基于非线性有限元模型更新的损伤识别方法的关键步骤是验证涉及大型和复杂非线性有限元模型的情况，这些模型需要估计大量参数。本文研究了无味卡尔曼滤波器（UKF）在更新这些类型的模型中的性能，并提出了一种分批递归变量以降低计算成本。此外，研究了考虑异构响应测量的影响。研究了涉及强非线性的大型和复杂有限元模型的两个应用示例，包括二维钢框架建筑和三维隔离桥，其中包含大量未知模型参数。发现在不牺牲估计性能的情况下，所提出的批量递归方法的大量计算时间节省。这证实了使用贝叶斯技术校准具有高维参数空间的实际系统的大型和复杂滞后有限元模型的可行性。此处获得的成功结果表明，所提出的方法代表了一种新颖且有前途的工具，可用于更新涉及大量参数的大型非线性结构有限元模型，而这些参数的校准可能会因常规更新技术而变得无法实现。

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