Current seismic codes do not incorporate a well-established methodology for the selection of passive dampers type and their topological distribution and properties along the height of structures. Achieving the intended performance is made more complicated when structures are subject to extreme events and operate well within their inelastic range. This thesis utilizes a self-organizing genetic algorithm (soGA) with probabilistic gene-by-gene crossover and an adaptive active ground motion subset scheme to efficiently find optimal designs of low-rise steel frames subject to large number of extreme ground motions. Different types of passive dampers were considered, while the steel frames were modeled using the modified Ibarra-Medina-Krawinkler deterioration model with bilinear hysteretic response. Optimal design topologies were identified for different types of dampers that satisfied predefined performance levels in terms of story drift and floor acceleration demand parameters. With the capability to consider an active ground motion subset scheme, the computational effort was significantly reduced without prohibiting soGA to find optimal design solutions that satisfy the performance levels for the full ground motion set.
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Master of Science (M.S.)
College of Engineering and Computer Science
Civil, Environmental, and Construction Engineering
Civil Engineering; Structures and Geotechnical Engineering
Length of Campus-only Access
Masters Thesis (Campus-only Access)
Wang, Tiancheng, "Seismic Design Optimization of Steel Structures Using Genetic Algorithm" (2020). Electronic Theses and Dissertations, 2020-. 311.