Predictability of Inverse Impact Force Location as Affected by Measurement Noise

The impact force localization inverse problem is considered through a nonlinear optimization procedure. The objective function is derived in the particular case of elastic structures for which Maxwell-Betti theorem holds. Additional geometric constraints were introduced in order to stabilize optimum search. The solution of the constrained non linear mathematical problem was performed by means of two outstanding evolutionary algorithms that include Genetic Algorithm and Particle Swarm Optimization. Focus was done on the robustness aspect of force impact localization predictability when an additive white noise is assumed to perturbed strain measurement. It was found that the Genetic Algorithm fails to track the exact solution independently from the noise level as an error was systematically present in the solution. On the other hand, the Particle Swarm Optimisation based algorithm performed very well even for noise levels as high as 2% of the measured strain signal.