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

基本信息

  • 批准号:
    1254338
  • 负责人:
  • 金额:
    $ 40万
  • 依托单位:
  • 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

项目摘要

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.
这项教师早期职业发展(职业)计划奖的目的是开发新的和改进的结构健康监测(SHM)方法,用于损害诊断和预后(估计其余用途寿命)。在美国基础设施的最新成绩单中,美国土木工程师学会将美国基础设施描述为维持不足,无法满足当前和未来的需求,在某些情况下是不安全的。扩大和改善SHM以进行损害评估和维护对于建立可持续和弹性的民用基础设施系统至关重要,并确保它们可以满足未来用户的需求。从SHM获得的关键信息为财务资源的最佳分配提供了基础,以维护,康复和加强基础设施。新方法还将允许对地震后的结构进行快速评估。这个职业项目将通过鼓舞人士,本科和K-12学生来整合研究和教育,以通过突出SHM领域的当前研究需求和机会来应对基础设施挑战。该项目将通过与SHM相关的研究来影响本科和研究生级别的学生。通过创建与SHM相关的基于乐高的夏季经验,并帮助教师将工程主题带到课堂上。该研究将集中在为基于振动的SHM开发新方法,基于对结构的非线性有限元模型的概率校准,使用对中等范围的非线性响应,例如中等层次的EcteReations,将介绍与K-12学生的宣传。在这种方法中,将从测得的输入输出非线性数据中标识时间变化的短时模态参数和/或结构的非线性正常模式。然后,这些确定的特征将通过确定性和概率(贝叶斯)模型更新方案来估计结构非线性模型的参数。最后,将使用数值模拟的数据以及可用的实验数据评估该方法的性能。该项目的教育组成部分将通过K-12外展,学生教育,研究生教育以及对这些教育目标的成果的评估进行。

项目成果

期刊论文数量(0)
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Babak Moaveni其他文献

One versus all: identifiability with a multi-hazard and multiclass building damage imagery dataset and a deep learning neural network
一对一:利用多危险和多类建筑损坏图像数据集和深度学习神经网络进行识别
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Olalekan R. Sodeinde;Magaly Koch;Babak Moaveni;L. Baise
  • 通讯作者:
    L. Baise
Inverse modeling of wind turbine drivetrain from numerical data using Bayesian inference
  • DOI:
    10.1016/j.rser.2022.113007
  • 发表时间:
    2023-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Mohammad Valikhani;Vahid Jahangiri;Hamed Ebrahimian;Babak Moaveni;Sauro Liberatore;Eric Hines
  • 通讯作者:
    Eric Hines
Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data
通过高斯过程进行虚拟传感,使用 SCADA 数据预测海上风力涡轮机的弯矩响应
  • DOI:
    10.1016/j.renene.2024.120466
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    8.7
  • 作者:
    Bridget Moynihan;E. M. Tronci;Michael C. Hughes;Babak Moaveni;Eric Hines
  • 通讯作者:
    Eric Hines
Hierarchical Bayesian quantification of aerodynamic effects on an offshore wind turbine under varying environmental and operational conditions
  • DOI:
    10.1016/j.ymssp.2024.112174
  • 发表时间:
    2025-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Mingming Song;Babak Moaveni;Eric Hines
  • 通讯作者:
    Eric Hines
Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements
  • DOI:
    10.1016/j.ymssp.2024.112183
  • 发表时间:
    2025-02-01
  • 期刊:
  • 影响因子:
  • 作者:
    Azin Mehrjoo;Eleonora M. Tronci;Bridget Moynihan;Babak Moaveni;Finn Rüdinger;Ross McAdam;Eric Hines
  • 通讯作者:
    Eric Hines

Babak Moaveni的其他文献

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{{ truncateString('Babak Moaveni', 18)}}的其他基金

PIRE: Multi-Domain, Multi-Scale, Policy-Aware Digital Twin for Offshore Wind Energy Infrastructure
PIRE:海上风能基础设施的多领域、多规模、政策感知数字孪生
  • 批准号:
    2230630
  • 财政年份:
    2023
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
An Adaptive System Identification Approach Using Mobile Sensors
使用移动传感器的自适应系统识别方法
  • 批准号:
    1903972
  • 财政年份:
    2019
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant
BRIGE: Continuous Structural Health Monitoring Framework for Bridge Structures
BRIGE:桥梁结构的连续结构健康监测框架
  • 批准号:
    1125624
  • 财政年份:
    2011
  • 资助金额:
    $ 40万
  • 项目类别:
    Standard Grant

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概率约束条件下非线性系统混合最优控制的数值算法设计、分析与应用
  • 批准号:
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  • 批准年份:
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非线性随机微分方程的解及其不变概率测度的数值近似算法
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非线性函数逼近强化学习的概率基础
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    2022
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    $ 40万
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    Postgraduate Scholarships - Doctoral
Probabilistic Foundations for Reinforcement Learning with Nonlinear Function Approximation
非线性函数逼近强化学习的概率基础
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