Pareto optimal sensor locations for structural identification

Abstract

Theoretical and computational issues arising in the selection of the optimal sensor configuration for parameter estimation in structural dynamics are addressed. The objective is to optimally locate sensors in the structure such that the resulting measured data are most informative for estimating the parameters of a family of mathematical model classes used for structural modeling. For a single model class, the information entropy is used as the optimality criterion for selecting the best sensor configuration. For multiple model classes, 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. A heuristic algorithm is proposed for constructing effective Pareto optimal sensor configurations that are superior, in terms of computational efficiency and accuracy, to the Pareto sensor configurations predicted by evolutionary algorithms suitable for solving general multi-objective optimisation problems. The theoretical developments and the effectiveness of the proposed algorithms are illustrated for a 10-DOF chain-like spring mass model and a 32-DOF truss structure.