Structural topology optimization under constraints on instantaneous failure probability

Junho Chun, Junho Song, Glaucio H. Paulino

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Accurate prediction of stochastic responses of a structure caused by natural hazards or operations of non-structural components is crucial to achieve an effective design. In this regard, it is of great significance to incorporate the impact of uncertainty into topology optimization of structures under constraints on their stochastic responses. Despite recent technological advances, the theoretical framework remains inadequate to overcome computational challenges of incorporating stochastic responses to topology optimization. Thus, this paper presents a theoretical framework that integrates random vibration theories with topology optimization using a discrete representation of stochastic excitations. This paper also discusses the development of parameter sensitivity of dynamic responses in order to enable the use of efficient gradient-based optimization algorithms. The proposed topology optimization framework and sensitivity method enable efficient topology optimization of structures under stochastic excitations, which is successfully demonstrated by numerical examples of structures under stochastic ground motion excitations.

Original languageEnglish (US)
Pages (from-to)773-799
Number of pages27
JournalStructural and Multidisciplinary Optimization
Issue number4
StatePublished - Apr 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

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


  • Discrete representation
  • Parameter sensitivity
  • Reliability based design optimization
  • Stochastic excitation
  • Topology optimization


Dive into the research topics of 'Structural topology optimization under constraints on instantaneous failure probability'. Together they form a unique fingerprint.

Cite this