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.

这个教师早期职业发展（CAREER）计划奖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）。在美国基础设施的最新报告中，美国土木工程师协会将美国基础设施描述为维护不善，无法满足当前和未来的需求，并且在某些情况下不安全。扩大和改善SHM的损害评估和维护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获得的关键信息为最佳分配财政资源以维护、修复和加强基础设施提供了基础。新方法还将允许在地震后对结构进行快速评估。这个职业生涯项目将通过激励研究生，本科生和K-12学生通过突出SHM领域当前的研究需求和机会来应对基础设施挑战，从而整合研究和教育。该项目将影响学生在本科和研究生阶段通过SHM相关的参与研究。通过创建与SHM相关的乐高暑期体验，以及帮助教师将工程主题带入课堂，实现对K-12学生的推广。研究将专注于开发基于振动的SHM的新方法，该方法基于结构的非线性有限元模型的概率校准，使用其测量的非线性响应对中到大振幅激励（如地震）进行响应。在这种方法中，时变的短时模态参数和/或非线性正常模式的结构将被识别从测量的输入输出非线性数据。这些识别的功能，然后将用于通过确定性和概率（贝叶斯）模型更新计划的结构的非线性模型的参数估计。最后，该方法的性能将使用数值模拟数据以及可用的实验数据进行评估。该项目的教育部分将通过K-12外展，本科生教育，研究生教育和这些教育目标的成果评估来进行。