An approach is presented to incorporate a multi-objective genetic algorithm (GA) optimization strategy for the evaluation of damage within a solid continuum. Through simulated test problems based on the characterization of internal pipe surface geometry (as could potentially be affected by a damage process) from steady-state dynamic measurements of outer surface displacement, the multi-objective GA is shown to provide substantial computational improvement over single-objective strategies. Furthermore, the multi-objective approach consistently traversed the optimization search space to efficiently produce more accurate characterization results and exhibited consistently better tolerance to measurement noise in contrast to the single-objective strategies. In general, the multi-objective approach maintains a high level of diversity in the solution population during the search process, thus being potentially better equipped to avoid local minima during the search process and identify multiple solutions where they exist.

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