Performance-based control co-design of building structures with controlled rocking steel braced frames via neural dynamic model

Sajad Javadinasab Hormozabad, Mariantonieta Gutierrez Soto

Research output: Contribution to journalArticlepeer-review

Abstract

Controlled rocking steel braced frames (CRSBFs) are modern systems for resilient building structures known for their effective energy dissipation and self-centering features. In a CRSBF, post-tensioned (PT) strands and shear fuses are the distinct features providing the self-centering and energy dissipation mechanisms. As a relatively new structural system, there is a need to study this system’s optimal design. Control co-design has gained interest in recent years as a class of integrated engineering system design methods as an alternative to the traditional approach of optimizing the structural design and sequentially optimizing the control system’s design. It considers the direct relationship between physical and control system design decisions to discover non-obvious design solutions that enable new performance and functionality levels. This paper proposes a performance-based control co-design methodology for building structures integrating CRSBFs that concurrently minimizes the mainframe weight and determines the CRSBF design parameters subject to design code requirements as the optimization constraints. The patented neural dynamic model of Adeli and Park is used in this research to solve the nonlinear optimization problem. The seismic performance evaluation of the proposed methodology includes a shear frame and a nonlinear 6-story 3D building structure. The 6-story building model consists of 282 nodes with 1,692 degrees of freedom. A total of 610 frame elements, 8 PT strands with nonlinear material, and 32 nonlinear links are determined without requiring high-performance computing. Results show that the energy dissipation mechanism effectively reduces the seismic demand in structural members. The proposed performance-based control co-design methodology can lead to 21% reduction in the total weight of the 6-story frame structure.

Original languageEnglish (US)
Pages (from-to)1111-1125
Number of pages15
JournalStructural and Multidisciplinary Optimization
Volume64
Issue number3
DOIs
StatePublished - Sep 2021

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization

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