Analysis, Design & Performance

Full Session with Abstracts

335009-4 - Performance-Based Topology Optimization of Dynamic and Uncertain Wind-Excited Tall Buildings

Saturday, April 21
10:00 AM - 11:30 AM
Location: 202A

To mitigate losses due to natural disasters, one of the main challenges that structural engineers are facing is the design of systems that not only satisfy society’s expectation on performance but also deal with limited resources. To achieve these goals, the combination of performance-based design and optimization provides an attractive approach. Indeed, this combination provides a means to set performance objectives in terms that are understood by decision makers while optimization allows for the systematic identification of systems that not only meet the performance objectives, but also minimize resources. Within this context, performance-based topology optimization (PBTO) seeks to find optimal lateral load resisting systems while ensuring performance over a full range of hazard levels. Over the past few years, there has been intense research towards the development of PBTO frameworks specifically for dynamic tall building systems subject to winds. There is, however, still a lack of tall building PBTO frameworks that can handle system-level performance constraints defined in terms of first excursion probabilities over the duration of the event. This work proposes a data-driven PBTO framework for uncertain and dynamic wind-excited tall building systems subject to system-level performance constraints. In particular, the performance of the system is defined in terms of first excursion constraints imposed on measures that account for both life safety as well as occupant comfort. To facilitate the use of gradient-based optimizers, equivalent constraints in the inverse form are defined in terms of the second order statistics of the response and a reduced-variate associated with the target failure probability. To ensure efficiency, the framework is set in the frequency domain while analytical expressions are derived for the sensitivities of the inverse constraints. A case study is presented to demonstrate the potential of the proposed framework.

Seymour M. Spence

Assistant Professor
University of Michigan

Seymour M.J. Spence is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. He joined the University of Michigan in September 2014 from the University of Notre Dame where he was a Research Assistant Professor. Spence’s history encompasses experiences in both academics and industry. His main research interests lie in the areas of system and component reliability and performance-based design, reliability-based design optimization, and wind engineering. His research centers on developing models that better predict and optimize the performance of the built environment to resist the actions of natural hazards. The core of his research is focused on the algorithmic and computational issues involved in reaching these goals.

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