Rotation-Based Multi-Particle Collision Algorithm with Hooke–Jeeves Approach Applied to the Structural Damage Identification

A hybrid metaheuristic combining the Multi-Particle Collision Algorithm (MPCA) with the Hooke–Jeeves (HJ) method is applied to identify structural damage. A new version of the MPCA is formulated with the rotation-based learning mechanism to the exploration search. The inverse problem of damage identification is formulated as an optimization problem assuming the displacement time history as experimental data. The objective function is the square difference between the measured displacement and the displacement calculated by the forward model. The approach was tested on a cantilevered beam structure. Time-invariant damages were assumed to generate the synthetic displacement data. Noiseless and noisy data were considered. Finite element method was used for solving the direct problem. The comparison with standard MPCA-HJ and the new version of the hybrid method are reported. The use of these hybrid algorithms allows to obtain good estimations using a full set of data, or using a reduced dataset with a low level of noise in data.

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