SENSOR PLACEMENT OPTIMIZATION USING ENSEMBLE KALMAN FILTER AND GENETIC ALGORITHM

Abstract eng:
A robust methodology is proposed in this study for determining the optimal locations of sensors in a structure to extract the most informative measurement data for the purpose of parameter estimations. First, the Ensemble Kalman Filter (EnKF) is used as a history matching method to predict and update the state and model parameters of the system. Then, a Genetic Algorithm (GA) approach is applied to determine the best locations of the sensors in the system through a minimization procedure, where the objective function to be minimized is represented by the mismatch between the predicted values and the actual measurements. The robustness and efficiency of the proposed method are demonstrated by developing the optimal sensor configuration for a shear building subjected to El Centro earthquake excitation at its base and using synthetic measurements of displacements and velocities of different floors.

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