Multi-objective differential evolution algorithm for stochastic system identification

Zhou, Jin; Mita, Akira; Li, Rongshuai

doi:10.1117/12.2006578

Cited by 1 publication

(1 citation statement)

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“…Using computational intelligence, several multiobjective parameter and damage identification methods have been reported recently. The multiobjective configurations include combinations of frequencies and mode shapes (Haralampidis et al., ; Perera and Ruiz, ), diverse measurement sets under multiple load cases (Jung et al., ), the discrepancy and variance of the stochastic responses (Zhou et al., ), and the output errors along with the stability measure of the discretized partial differential equations (Talic et al., ). From these publications, it is indicated that the multiobjective constrained optimization may be necessary for parameter identification of large‐scale civil structures (Jiang and Adeli, ; Jiang and Adeli, , ; Catbas and Kijewski‐Correa, ; Amezquita‐Sanchez and Adeli, ).…”

Section: Introductionmentioning

confidence: 99%

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

Shan

Ouyang

Yuan

et al. 2016

Computer aided Civil Eng

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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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