GA-Based Decision Support for Optimizing the Response of Secondary Systems

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dc.contributor.advisor Dr. John W. Baugh, Jr., Committee Member en_US
dc.contributor.advisor Dr. G. (Kumar) Mahinthakumar, Committee Member en_US
dc.contributor.advisor Dr. Abhinav Gupta, Committee Chair en_US
dc.contributor.author Kripakaran, Prakash en_US
dc.date.accessioned 2010-04-02T17:53:18Z
dc.date.available 2010-04-02T17:53:18Z
dc.date.issued 2002-07-22 en_US
dc.identifier.other etd-07062002-155126 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/120
dc.description.abstract The objective of this research is to develop a Decision Support System (DSS) for seismic design and performance evaluation of piping supports. The current practice of designing piping support locations is primarily heuristic, relying heavily on professional experience. In this thesis, approaches to optimize support locations using Genetic Algorithms (GAs), a heuristic optimization technique, are discussed. These approaches have been implemented in a DSS using Vitri, a generic, distributed framework designed to support the development of DSSs, which reduces the computational requirements by combining the processing power of a network of workstations. Previous attempts to solve the problem of pipe support optimization modeled supports as flexible springs, which have a stiffness depending on the support capacity, resulting in the use of an integer representation in the GA. In this thesis, a new approach where supports are modeled as rigid springs is presented. This permits the use of a binary representation in the GAs. Also, earlier attempts had solved the problem by minimizing the number of supports, which does not always indicate if cost is minimized. In this thesis, capital cost and lifetime cost are studied by examining the trade-off curve between the cost and the number of supports. A crossover scheme aimed at generating cost optimal solutions of a specified number of supports, which is required for generating trade-off curves, is proposed. It has been observed that optimization results in solutions that may be practically infeasible because of unmodeled costs in the optimization model. In pipe support optimization, such costs might be from the preference of certain locations over others because of easier support installation costs or the desire to locate the supports under lumped masses to stabilize the pipe against local vibrations from equipment such as pumps and motors. The role of Modeling to Generate Alternatives (MGA), a methodology based on optimization to produce alternatives, is explored to address these issues. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Genetic Algorithms en_US
dc.subject Pipe support optimization en_US
dc.subject Decision Support Systems en_US
dc.subject MGA en_US
dc.title GA-Based Decision Support for Optimizing the Response of Secondary Systems en_US
dc.degree.name MS en_US
dc.degree.level thesis en_US
dc.degree.discipline Civil Engineering en_US


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