GA-Based Optimization of Steel Moment Frames: A Case Study

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Title: GA-Based Optimization of Steel Moment Frames: A Case Study
Author: Hall, Brian Scott
Advisors: Emmett Sumner, Committee Member
John Baugh, Committee Member
Abhinav Gupta, Committee Chair
Abstract: Moment resisting frames are utilized in steel construction in order to provide overall stability and to withstand lateral forces related to wind and earthquake loadings. While the industry focuses on producing more economical moment frames by optimizing for the weight of steel, it should be noted that because of present trends the labor cost associated with rigid moment connection fabrication governs the total cost of these frames. Therefore, the objective of this case study is to discover alternative ways of producing more cost-effective moment frames without compromising overall stability. This is achieved by considering both the cost of steel and the cost of connections within the design process. In order to reduce cost, rigid connections within the frame are replaced with standard pinned connections, and member sizes are increased where needed, a method unlike the current least-weight design approach. Optimization techniques are developed in order to identify the most advantageous locations of the remaining moment connections. A Genetic Algorithm, interfaced with a Java-based frame analysis program, is utilized to produce these optimal solutions. At the present time, the consideration of connection-related costs is crucial in determining the most cost effective moment frames. By developing an optimization technique that considers both the number of moment connections and the total weight of the frame, the least weight optimal solution can be improved upon drastically, with an almost 50% reduction in the total cost and an approximately 60% increase in total weight. The unorthodox connection arrangements produced are important, as they provide alternative designs that, while functional, are not currently explored by those in practice.
Date: 2006-03-07
Degree: MS
Discipline: Civil Engineering
URI: http://www.lib.ncsu.edu/resolver/1840.16/2022


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