CAREER: Multi-Objective Optimization of Sensor Placement for Reliable Monitoring and Control of Structures

职业：多目标优化传感器放置以实现结构的可靠监测和控制

基本信息

批准号：
1750225
负责人：
Lauren Linderman
金额：
$ 50万
依托单位：
University of Minnesota-Twin Cities
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750225&HistoricalAwards=false
关键词：
CAREER Multi Objective Optimization Sensor

项目摘要

This Faculty Early Career Development Program (CAREER) grant aims to sustain the long-term performance of civil infrastructure by identifying the most effective measurement types and locations for monitoring and isolating structural response. Continued performance of civil structures in daily use and after a natural hazard event is critical to the resilience of our communities and the US economy. The integration of sensor networks and physical systems is essential for maintaining this continued performance of civil structures while facing challenges in aging, energy, and the environment. The interaction of these cyber-physical components impacts almost every endeavor today and is indispensable in tackling today's broader challenges and continued US economic growth. This research project will identify optimal network systems for infrastructure applications through the integration of virtual and physical sensors and actuators. Unlike current approaches that are limited to specific measurement technology, function, and/or application, the new network approach is scalable to monitor large structures, as well as to provide insight on performance limits and maximize the reliability of the selected network configuration. Experimental modules will be developed based on the framework of this research through student's design projects and will be used to create an interactive outreach platform for students in secondary school.Effective implementation of sensor and actuator networks necessitates that limited sensor resources provide the most informative measured data and are reliable in the face of uncertainties. The objective of this research is a framework to identify the bounds of algorithm performance for structural health monitoring and control applications through optimization of sensor placement, type, and configuration in the presence of uncertainties. The multi-objective optimization sensor approach will be validated with results from physical tests ranging from laboratory-scale monitoring and control experiments through in-situ deployments. Key results include: (i) a sparsity-promoting algorithm to determine near-optimal sensor requirements for effective response and parameter estimation, (ii) insight into the performance bounds for estimation-based algorithms, and (iii) a novel experimental approach for validation of the technique under repeated, non-idealized conditions to establish the reliability of estimation-based approaches.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该教师早期职业发展计划（CAREER）赠款旨在通过确定用于监测和隔离结构响应的最有效的测量类型和位置来维持土木基础设施的长期性能。土木结构在日常使用中和自然灾害事件后的持续性能对于我们社区和美国经济的恢复能力至关重要。传感器网络和物理系统的集成对于在面临老化、能源和环境挑战的同时保持土木结构的持续性能至关重要。这些网络物理组件的相互作用几乎影响着当今的每一项努力，并且对于应对当今更广泛的挑战和持续的美国经济增长是不可或缺的。该研究项目将通过集成虚拟和物理传感器和执行器来确定基础设施应用的最佳网络系统。与仅限于特定测量技术、功能和/或应用的当前方法不同，新的网络方法可扩展以监控大型结构，并提供对性能限制的洞察并最大限度地提高所选网络配置的可靠性。实验模块将基于本研究的框架，通过学生的设计项目进行开发，并将用于为中学生创建一个互动推广平台。传感器和执行器网络的有效实施需要有限的传感器资源提供最丰富的测量数据。并且在面对不确定性时是可靠的。本研究的目标是建立一个框架，通过在存在不确定性的情况下优化传感器放置、类型和配置来确定结构健康监测和控制应用的算法性能界限。多目标优化传感器方法将通过从实验室规模监测和控制实验到现场部署的物理测试结果进行验证。主要结果包括：(i) 一种稀疏性促进算法，用于确定有效响应和参数估计的接近最佳传感器要求，(ii) 深入了解基于估计的算法的性能范围，以及 (iii) 一种新颖的验证实验方法该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。