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

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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.