Optimal instrumentation strategies for structural identification

A methodology is presented for designing cost-effective optimal sensor/actuator configurations and excitation characteristics useful for structural model updating and health monitoring purposes. The optimal sensor and actuator. configuration is selected such that the resulting measured data are most informative about the condition of the structure. This selection is based on an information entropy measure of the uncertainty in the model parameter estimates obtained using a statistical system identification methodology. The optimal instrumentation problem is formulated as an optimization problem involving mixed continuous discrete variables. The discrete variables are related to the number and location of sensors, while the continuous variables are related to the excitation characteristics such as amplitude and frequency content. The optimization problem is solved efficiently using genetic algorithms. This study also addresses important issues related to robustness of the optimal sensor and actuator configuration to uncertainties in the structural model, as well as issues related to the optimal sensor and actuator configurations designed to monitor multiple damage scenarios. The theoretical developments are illustrated by designing the optimal configuration for a 32-DOF two-dimensional truss structure subjected to an impulse hammer excitation.