Please use this identifier to cite or link to this item: https://knowledgecommons.lakeheadu.ca/handle/2453/5298
Title: Structural layout optimization framework of tall buildings subjected to wind load
Authors: Alanani, Magdy
Keywords: Topology optimization;Tall buildings;Wind load;Genetic algorithm;Computational Fluid Dynamics (CFD);Finite Element Method (FEM);Performance-based Wind Design (PBWD)
Issue Date: 2024
Abstract: Conventional design methodology for tall buildings is a time-consuming and repetitive trial-anderror procedure with a limited probability of yielding an optimal solution that satisfies architectural, structural and serviceability requirements. Tall buildings are typically slender structures and mainly depend on a Main Wind Force Resisting System (MWFRS) (e.g., shear walls, cores, and bracing systems) to withstand the lateral load of wind events, where a minor change in their layout, size, or shape will affect the cost tremendously. Consequently, a structural layout optimization procedure will result in a more economical and sustainable design. Most previous studies focused on developing optimization frameworks and algorithms that rely on using static wind loads. Even with the adoption of dynamic wind load, the focus was on the vertical layout of the lateral load-resisting systems in a simplified form and as a single objective optimization due to the demanding computational costs. Therefore, the first and main objective of this research is to develop a novel structure-wind optimization framework (SWOF) to find the optimal horizontal (e.g., shear wall) layout of tall buildings subjected to wind loads. SWOF is considered a genetic algorithm-based framework that uses a data-driven surrogate model to evaluate its constraints and objective functions. These surrogate models rely on a training dataset prepared using the Finite Element Method (FEM), which has been created using an open application program interface (OAPI) MATLAB code. [...]
URI: https://knowledgecommons.lakeheadu.ca/handle/2453/5298
metadata.etd.degree.discipline: Engineering : Civil
metadata.etd.degree.name: Doctor of Philosophy
metadata.etd.degree.level: Doctoral
metadata.dc.contributor.advisor: Elshaer, Ahmed
metadata.dc.contributor.committeemember: Gong, Yanglin
Wang, Wilson
Appears in Collections:Electronic Theses and Dissertations from 2009

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