Model Updating of Structural Systems with Incomplete Information: Global Identifiability, Optimal Instrumentation and Uncertainty Quantification

不完整信息结构系统的模型更新：全局可识别性、最优仪表和不确定性量化

• 批准号: 1363360
• 负责人: Raimondo Betti
• 金额: $ 23.65万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1363360&HistoricalAwards=false
• 关键词: Model; Updating; Structural; Systems; Incomplete

Civil infrastructure systems form the backbone of the economic well-being and progress of any nation. The field of Structural Health Monitoring (SHM) addresses the proper maintenance and timely rehabilitation of such systems, thereby ensuring their continued functionality. In SHM, having a reliable model of the structure is essential. Usually, Finite Element models are used to predict how actual systems, e.g. bridges and buildings, behave under various conditions, from day-to-day service conditions (e.g. traffic) to extreme events (e.g. earthquakes, hurricanes). However, there is a certain level of uncertainty inherent, for example, to the modeling assumptions used by the engineer. In addition, a system changes its characteristics with time due to aging and seasonal effects, and/or sudden extreme events. The aim of model updating is to correct the physical parameters of a model of a structure using information obtained from the real structure so as to get an updated model accurately describing the current condition of the system. This updated model can then be used for predictive, health monitoring and reliability analyses purposes. The importance of this project is in its treatment of constraints related to practical implementation, e.g. not having a sufficient number of sensors, which could have serious implications and could consequently invalidate the results of the entire representation of the model. In the project, a model updating strategy will be conducted according to the following steps. First, tests for global ascertaining and consequent constraints on instrumentation requirements will be formulated, providing minimal instrumentation set-ups to guarantee unique identification. Next, a mode shape normalization scheme will be developed to estimate the complete normalized modal information, which will then be used to estimate the system's physical parameters. Efficient modal parameter comparative measures will be used for mode matching purposes and ways to account for unobserved modes will also is investigated. To account for the uncertainties introduced by uncertain information about the system, environmental fluctuations and the inevitable presence of measurement noise, the algorithm will be extended into a stochastic framework using Bayesian inference as well as an alternative sensitivity based approach to characterize the uncertainty propagation at different steps. The goal of the project is to represent the structure in its present condition for analysis.

民用基础设施系统是任何国家经济福祉和进步的支柱。结构健康监测（SHM）领域致力于此类系统的适当维护和及时修复，从而确保其持续功能。在结构健康管理中，有一个可靠的结构模型是必不可少的。通常，有限元模型用于预测实际系统（例如桥梁和建筑物）在各种条件下的行为，从日常服务条件（例如交通）到极端事件（例如地震，飓风）。然而，存在一定程度的固有不确定性，例如工程师使用的建模假设。此外，由于老化和季节效应，和/或突然的极端事件，系统会随时间改变其特性。模型修正的目的是利用从真实的结构中获得的信息对结构模型的物理参数进行修正，从而得到准确描述系统当前状态的修正模型。然后，该更新的模型可以用于预测、健康监测和可靠性分析目的。该项目的重要性在于其对与实际实施有关的约束的处理，例如，没有足够数量的传感器，这可能会产生严重的影响，并可能因此使模型的整个表示的结果无效。在本项目中，将按照以下步骤执行模型更新策略。首先，将制定对仪表要求的全球确定和随之而来的约束的测试，提供最小的仪表设置，以保证唯一的识别。接下来，将开发振型归一化方案来估计完整的归一化模态信息，然后将其用于估计系统的物理参数。有效的模态参数的比较措施将用于模式匹配的目的和方式来考虑未观察到的模式也将进行调查。考虑到由系统的不确定信息、环境波动和不可避免的测量噪声所引入的不确定性，该算法将扩展到一个随机框架中，使用贝叶斯推理以及基于灵敏度的替代方法来表征不同步骤的不确定性传播。该项目的目标是代表结构在其目前的分析条件。