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

Principal Investigator:

Lauren Linderman, Assistant Professor, Civil, Environmental and Geo-Engineering

Project Summary:

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 communities and the U.S. 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 U.S. economic growth. This research project is identifying 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. It can also provide insight on performance limits and maximize the reliability of the selected network configuration. Experimental modules are being developed based on the framework of this research through students' 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: 1) a sparsity-promoting algorithm to determine near-optimal sensor requirements for effective response and parameter estimation, 2) insight into the performance bounds for estimation-based algorithms, and 3) 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 the National Science Foundation's (NSF's) statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Sponsor:

Project Details:

  • Start date: 08/2018
  • Project Status: Active
  • Research Area: