Algorithm-Fused High Performance Damage Detector: Optimal Sensor Distributions

该项目将重点开发一种强大的自动化方法来识别结构系统中的损坏。研究的重点是：1)互补算法的融合；2)融合集合的最优传感器分布。补充性算法的选择涉及识别其对损害和对云损害检测因损害情景和运行条件而不同的来源的敏感性的方法。在第一阶段，该项目检查基于残差相关性的检测过滤器与使用幅度相关残差度量的过滤器的融合。这项研究希望证明，优化的融合检测器将具有一个损害检测阈值，对于固定的虚警概率，该阈值明显好于单独算法的检测阈值。与算法融合密切相关的是在给定融合询问方案的情况下，选择最优的传感器布局的研究。分析工作完成后，研究进入实验阶段，在四分之一尺度的钢结构上测试融合算法的性能，该结构暴露在天气中，因此受到现实环境变化的影响。算法融合在自动目标识别和各种其他领域被证明是卓有成效的，但在本项目中首先在结构健康监测的背景下进行了系统的检查。如果成功，这项研究不仅将为土木结构的应用提供一种稳健的损伤检测方案，而且还将指出算法融合在其他目标如损伤定位和量化等方面的优点。与该项目相关的教育活动包括：1)通过介绍基于项目主题的为期数周的研究活动，与一所优秀的本科工程学院--奥林学院进行互动；2)参与女孩联系计划，这是一个针对波士顿地区中学女孩的科技外展计划；3)下午参加哈佛大学S医学院探索计划的实践活动，每年秋天来自剑桥和波士顿的200多名中学生参加该计划。从事该项目的研究生还将接受关于土木结构损伤检测主题的高级培训，这一主题具有很高的工程重要性。

The project will focus the development of a robust automated approach for identifying damage in structural systems. The research thrusts are: 1) fusion of complementary algorithms and 2) optimal sensor distributions for the fused set. Selection of complementary algorithms involves identification of methods whose sensitivity to damage and to the sources that cloud damage detection differs with damage scenarios and operating conditions. In a first phase the project inspects the fusion of detection filters that operate on residual correlations with filters that work with amplitude dependent residual metrics. The research expects to demonstrate that the optimized fused detector will have a damage detection threshold that, for a fixed probability of false alarm, is significantly better than that of the individual algorithms. Intimately connected with the algorithmic fusion is research on the selection of sensor layouts that are optimal, given the fused interrogation scheme. Following the analytical work the research progresses into an experimental phase where the performance of the fused algorithms is tested on a one quarter scale steel structure that is exposed to the weather and thus subjected to realistic environmental changes.Algorithm fusion has proven fruitful in Automatic Target Recognition and various other areas but a systematic examination in the context of Structural Health Monitoring is first carried out in this project. If successful, this research will not only offer a robust damage detection scheme for applications to civil structures but it will also point to the merit of algorithmic fusion for other objectives such as the localization and the quantification of damage. Educational activities connected with the project include: 1) interactions with Olin College, an undergraduate engineering school of excellence, through introduction of multi-week research activities based on topics from the project 2) involvement with the program Girls Get Connected (GGC), a science and technology outreach for middle school girls in the Boston area and 3) an afternoon of hands-on activities on the Harvard?s Medical School explorations program, which is attended each fall by over 200 middle school students from Cambridge and Boston. The graduate student working on the project will also receive advanced training on the topic of damage detection in civil structures which is of high engineering importance.