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.

民用基础设施系统构成了任何国家的经济福祉和进步的骨干。结构健康监测（SHM）领域涉及此类系统的适当维护和及时的康复，从而确保了它们的持续功能。在SHM中，具有可靠的结构模型至关重要。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）。但是，例如，工程师使用的建模假设存在一定固有的不确定性。此外，由于衰老和季节性影响和/或突然的极端事件，系统随时间而改变其特征。模型更新的目的是使用从实际结构获得的信息纠正结构模型的物理参数，以便获得更新的模型，以准确描述系统的当前状况。然后，该更新的模型可以用于预测，健康监测和可靠性分析。该项目的重要性在于它处理与实际实施相关的约束，例如没有足够数量的传感器，这可能具有严重的影响，因此可能使模型的整个表示结果无效。在项目中，将根据以下步骤进行模型更新策略。首先，将制定针对仪器要求的全球确定和随之而来的限制的测试，从而提供最少的仪器设置，以保证唯一的识别。接下来，将制定模式形状归一化方案以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。有效的模态参数比较度量将用于模式匹配目的，还将研究未观察到的模式的方法。为了说明有关系统，环境波动和不可避免的测量噪声的不确定信息所引入的不确定性，该算法将扩展到使用贝叶斯推论以及基于替代灵敏度的方法以表征不同步骤的不确定性传播的替代灵敏度方法的随机框架。该项目的目的是在目前的分析条件下代表结构。