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A two-stage dual-objective structural identification method is presented in this article. The complexity of the identification of story-level physical models for large-scale building structures is first addressed through a comparative study. A stiffness variation-based stabilizing objective is proposed to be necessarily incorporated into iterative optimization with the classical performance objectives to improve the model feasibility, and an area-type evaluation index is subsequently proposed for the stopping criteria. Accordingly, a two-stage differential evolution-based dual-objective optimization framework is presented for the computation of Pareto fronts for nondominated candidate solutions. Then, the proposed method is investigated using two illustrative examples, including a nine-story benchmark structure, and a real-world seven-story reinforced concrete structure. A series of condensed models are identified from the nondominated solutions on the Pareto front. The prediction performance of the single-objective optimal model and the dual-objective acceptable models is compared using the overall discrepancies of acceleration, interstory drift, and modal properties, within both estimation and

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