Optimal experimental design for structural health monitoring applications

Successful structural health monitoring and condition assessment depends to a large extent on the sensor and actuator networks place on the structure as well as the excitation characteristics. An optimal experimental design methodology deals with the issue of optimizing the sensor and actuator network, as well as the excitation characteristics, such that the resulting measured data are most informative for monitoring the condition of the structure. Theoretical and computational issues arising in optimal experimental design are addressed. The problem is formulated as a multi-objective optimization problem of finding the Pareto optimal sensor configurations that simultaneously minimize appropriately defined information entropy indices related to monitoring multiple probable damage scenarios. Asymptotic estimates for the information entropy, valid for large number of measured data, are used to rigorously justify that the selection of the optimal experimental design can be based solely on nominal structural models associated with the probable damage scenarios, ignoring the details of the measured data that are not available in the experimental design stage. Heuristic algorithms are proposed for constructing effective, in terms of accuracy and computational efficiency, sensor configurations. The effectiveness of the proposed method is illustrated by designing the optimal sensor configurations for monitoring damage on a shear model of a building structure.