Seismic Data-Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

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 validation cases. Incorporation of the noise effect into the method is finally studied and discussed.