CAREER: Probabilistic Nonlinear Structural Identification for Health Monitoring of Civil Structures

职业：土木结构健康监测的概率非线性结构识别

• 批准号: 1254338
• 负责人: Babak Moaveni
• 金额: $ 40万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254338&HistoricalAwards=false
• 关键词: CAREER; Probabilistic; Nonlinear; Structural; Identification

The objective of this Faculty Early Career Development (CAREER) program award is to develop new and improved structural health monitoring (SHM) methods for damage diagnosis and prognosis (estimating the remaining useful life) of structures. In the latest report card for America's infrastructure, the American Society of Civil Engineers described U.S. Infrastructure as poorly maintained, unable to meet current and future demands, and, in some cases, unsafe. Expanding and improving SHM for damage assessment and maintenance is essential for establishing sustainable and resilient civil infrastructure systems and ensuring they can meet the needs of future users. The critical information obtained from SHM provides a basis for optimum allocation of financial resources towards the maintenance, rehabilitation and strengthening of the infrastructure. The new methodology will also allow rapid assessment of structures after an earthquake. This CAREER project will integrate research and education by inspiring graduate, undergraduate and K-12 students to take on the infrastructure challenges through highlighting current research needs and opportunities in the field of SHM. The project will impact students at the undergraduate and graduate levels through SHM-related involvement in the research. Outreach to K-12 students will be achieved by creating a LEGO-based summer experience related to SHM as well as helping teachers bring engineering topics to classrooms.The research will focus on developing a new methodology for vibration-based SHM, based on probabilistic calibration of nonlinear finite element models of structures using their measured nonlinear response to moderate to large amplitude excitations such as earthquakes. In this method, time-varying short-time modal parameters and/or nonlinear normal modes of a structure will be identified from measured input-output nonlinear data. These identified features will then be used to estimate parameters of a nonlinear model of the structure through deterministic and probabilistic (Bayesian) model updating schemes. Finally, the performance of this method will be evaluated using numerically simulated data as well as available experimental data. The educational component of this project will be performed through K-12 outreach, undergraduate student education, graduate student education, and evaluation of outcomes of these educational goals.

该学院早期职业发展（CAREER）计划奖项的目标是开发新的和改进的结构健康监测（SHM）方法，用于结构的损伤诊断和预后（估计剩余使用寿命）。美国土木工程师协会在最新的美国基础设施成绩单中称，美国基础设施维护不善，无法满足当前和未来的需求，在某些情况下甚至不安全。扩展和改进用于损害评估和维护的健康管理对于建立可持续和有弹性的民用基础设施系统并确保它们能够满足未来用户的需求至关重要。从 SHM 获得的关键信息为优化财政资源分配以维护、修复和加强基础设施提供了基础。新方法还可以对地震后的结构进行快速评估。该职业项目将通过强调健康管理领域当前的研究需求和机遇，激励研究生、本科生和 K-12 学生应对基础设施挑战，从而将研究和教育结合起来。该项目将通过与健康管理相关的研究参与来影响本科生和研究生水平的学生。将通过创建与 SHM 相关的基于乐高的夏季体验以及帮助教师将工程主题带入课堂来实现对 K-12 学生的推广。该研究将重点开发一种基于概率校准的基于振动的 SHM 新方法使用测量的非线性响应来建立结构的非线性有限元模型，以中到大幅度激励（例如地震）。在该方法中，将从测量的输入-输出非线性数据中识别结构的时变短时模态参数和/或非线性简正模态。然后，这些识别出的特征将用于通过确定性和概率（贝叶斯）模型更新方案来估计结构非线性模型的参数。最后，将使用数值模拟数据以及可用的实验数据来评估该方法的性能。该项目的教育部分将通过 K-12 外展、本科生教育、研究生教育以及对这些教育目标成果的评估来进行。