Algorithm-Fused High Performance Damage Detector: Optimal Sensor Distributions
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.
Northeastern University’s College of Engineering is home to numerous federally-funded research centers and an array of leading-edge projects and initiatives that advance discovery and new knowledge in health, sustainability, and security.