Theses and Dissertations

Issuing Body

Mississippi State University


Younan, Nicholas

Committee Member

Du, Q. Jenny

Committee Member

Follett, F. Randolph

Committee Member

Frazier, wm. Garth

Date of Degree


Document Type

Dissertation - Open Access


Electrical Engineering

Degree Name

Doctor of Philosophy


James Worth Bagley College of Engineering


Department of Electrical and Computer Engineering


The purpose of this dissertation is to demonstrate the ability to design health monitoring systems from a systematic perspective and how, with proper sensor and actuator placement, damage occurring in a structure can be detected and tracked. To this end, a design optimization was performed to determine the best locations to excite the structure and to collect data while using the minimum number of sensors. The type of sensors used in this design optimization was uni-axis accelerometers. It should be noted that the design techniques presented here are not limited to accelerometers. Instead, they allow for any type of sensor (thermal, strain, electromagnetic, etc.) and will find the optimal locations with respect to defined objective functions (sensitivity, cost, etc.). The use of model-based optimization techniques for the design of the monitoring system is driven by the desire to obtain the best performance possible from the system given what is known about the system prior to implementation. The use of a model is more systematic than human judgment and is able to take far more into account by using information about the dynamical response of a system than even an experienced structural engineer. It is understood in the context of structural modeling that no model is 100\% accurate and that any designs produced using model-based techniques should be tolerant to modeling errors. Demonstrations performed in the past have shown that poorly placed sensors can be very insensitive to damage development. To perform the optimization, a multi-objective genetic algorithm (GA) was employed. The objectives of the optimization were to be highly sensitive to damage occurring in potential “hot spots” while also maintaining the ability to detect damage occurring elsewhere in the structure and maintaining robustness to modeling errors. Two other objectives were to minimize the number of sensors and actuators used. The optimization only considered placing accelerometers, but it could have considered different type of sensors (i.e. strain, magneto-restrictive) or any combination thereof.



Genetic Algorithm||Structural Health Monitoring||Optimization||Health Monitoring Design