BRIGE: Multiscale Model-Data Fusion for Structural Health Monitoring of Fracture Critical Structures

BRIGE：用于断裂关键结构的结构健康监测的多尺度模型数据融合

• 批准号: 1342190
• 负责人: Eric Hernandez
• 金额: $ 17.5万
• 依托单位: University of Vermont & State Agricultural College
• 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: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1342190&HistoricalAwards=false
• 关键词: BRIGE; Multiscale; Model; Data; Fusion

Non-Technical Descripition of the Project's SignificanceThis Broadening Participation Research Initiation Grant (BRIGE) provides funding to develop a framework for structural health monitoring, diagnosis and prognosis of fracture critical structures. Fracture critical structures are those in which the failure of a single component can generate the failure of the complete system or a large portion of it. Recent catastrophic failures of these types of structures, especially bridges, have highlighted the need for a new and transformative approach to the problem. This project will develop the computational and data analysis tools necessary to continuously monitor fracture critical structures and help prevent such failures. Technical Description of the ProjectThe multiscale model-data fusion framework relies on a series of models, which represent the mechanical behavior of the structure at various scales of interest, and vibration measurements of local and global structural response. The project will develop algorithms capable of optimally combining the predictive capabilities of multiscale finite element models and sensor measurements, to reconstruct in real-time the complete response of the structure. The reconstructed response allows assessment of the current state of cumulative fatigue damage throughout the structure, thus anticipating potential damage before it reaches a critical level. The estimated damage condition, with its associated uncertainty is projected into the future and an estimate of the structural reliability can be obtained. The research involves development of computational algorithms, laboratory experiments and field validation using real data from an instrumented operational bridge in Vermont. The methods developed in this project will aide engineers to perform smarter early diagnosis and predictive maintenance of a multitude of conventional and non-conventional structural systems spanning civil, mechanical, biomedical and electrical applications. This project will also provide an opportunity to foster collaborations and potential applications of the research idea into medical diagnosis. Activities to Broaden the Participation of Underrepresented Groups in EngineeringFunding from this project will enable the principal investigator to recruit under-represented minorities into engineering education, research and mentoring. It will also allow him to undertake K-12 activities aimed at educating students about engineering, diversity and its critical importance in our society. This project will facilitate collaborations with existing initiatives at the University of Vermont aimed at recruiting undergraduate and graduate students, especially female and Latinos interested in pursuing careers in engineering. The PI will work with various student groups, such as the chapters of the Society of Women Engineers and American Society of Civil Engineers, which have volunteered to take part in sharing the engineering experience with K-12 students. These groups will join the PI in visiting local schools and hosting recruiting and educational activities at the university. The educational component of the project involves the development of a hands-on structural engineering lab designed to help students appreciate the role of models and sensors in understanding the behavior of structures. The PI will also develop a multidisciplinary course titled Reliability of Engineering System aimed at graduate students from all engineering disciplines. This course will incorporate data and findings from the research project into the course learning objectives.This research has been funded through the Broadening Participation Research Initiation Grants in Engineering solicitation, which is part of the Broadening Participation in Engineering Program of the Engineering Education and Centers Division.The research is also funded through the Experimental Program to Stimulate Competitive Research (EPSCoR), which is part of the Office of International and Integrative Activities.

项目重要性的非技术性描述这一扩大参与研究启动补助金（BRIGE）提供资金，以开发一个框架，用于结构健康监测、诊断和预测断裂关键结构。断裂临界结构是指一个单一构件的失效可能导致整个系统或大部分系统失效的结构。最近这类结构，特别是桥梁的灾难性失效，突出了对解决这一问题的新的变革性方法的需求。该项目将开发必要的计算和数据分析工具，以持续监测断裂关键结构，并帮助防止此类故障。项目技术描述多尺度模型数据融合框架依赖于一系列模型，这些模型代表了不同感兴趣尺度下结构的力学行为，以及局部和全局结构响应的振动测量。该项目将开发能够最佳结合多尺度有限元模型和传感器测量的预测能力的算法，以实时重建结构的完整响应。重建的响应允许评估整个结构的累积疲劳损伤的当前状态，从而在达到临界水平之前预测潜在的损伤。估计的损伤条件，其相关的不确定性预计到未来的结构可靠性的估计可以得到。该研究包括计算算法的开发，实验室实验和现场验证使用的真实的数据从佛蒙特州的仪表操作桥梁。该项目中开发的方法将帮助工程师对众多传统和非传统结构系统进行更智能的早期诊断和预测性维护，这些系统涵盖民用，机械，生物医学和电气应用。该项目还将提供一个机会，以促进合作和潜在的应用研究理念到医疗诊断。该项目的资金将使首席研究员能够招募代表性不足的少数民族参加工程教育，研究和指导。这也将使他能够开展K-12活动，旨在教育学生有关工程，多样性及其在我们社会中的至关重要性。该项目将促进与佛蒙特大学现有举措的合作，旨在招募本科生和研究生，特别是有兴趣从事工程职业的女性和拉丁美洲人。PI将与各种学生团体合作，例如女性工程师协会和美国土木工程师协会的分会，这些分会自愿参加与K-12学生分享工程经验。这些团体将与PI一起访问当地学校，并在大学举办招聘和教育活动。该项目的教育部分涉及开发一个实践结构工程实验室，旨在帮助学生理解模型和传感器在理解结构行为方面的作用。PI还将开发一门多学科课程，名为“工程系统可靠性”，面向所有工程学科的研究生。本课程将把研究项目中的数据和发现纳入课程学习目标。本研究已通过工程学中的扩大参与研究启动赠款征集获得资助，该研究是工程教育和中心司工程学中的扩大参与计划的一部分。本研究还通过刺激竞争研究的实验计划（EPSCoR）获得资助，它是国际和综合活动办公室的一部分。