Testing the impact of using cumulative data with genetic algorithms for the analysis of building energy performance and material cost

dc.contributor.advisor Ashuri, Baabak
dc.contributor.author Dingwall, Austin Gregory en_US
dc.contributor.committeeMember Castro-Lacouture, Daniel
dc.contributor.committeeMember Farrow, Robert
dc.contributor.department Building Construction en_US
dc.date.accessioned 2013-01-17T22:06:48Z
dc.date.available 2013-01-17T22:06:48Z
dc.date.issued 2012-11-14 en_US
dc.description.abstract The demand for energy and cost efficient buildings has made architects and contractors more aware of the resources consumed by the built environment. While the actual economic and environmental costs of future construction can never be completely predicted, energy simulations and cost modeling have become accepted ways to guide the design and construction process by comparing possible outcomes. These tools are now commonplace in the construction industry, and researchers are continuing to develop new and innovative strategies to optimize building design and construction. Previous research has proven that genetic algorithms are effective methods to evaluate and optimize building design in situations that contain a large number of possible solutions. The technique makes a computationally difficult multi-optimization process possible but is still a reactive and time consuming process that focuses on evaluation rather than solution generation. This research presented in this paper builds upon established multi-objective optimization techniques that use an energy simulator to estimate a conceptual building’s energy use as well as construction cost. The study compares simulations of a simplified model of a 3-story inpatient hospital located in Atlanta, Georgia using a defined set of variables. A combined global minimum of annual energy consumption and total construction is sought after using a method that utilizes a genetic algorithm. The second phase of this research uses a modified approach that combines the traditional genetic algorithm with a seeding method that utilizes previous results. A new set of simulations were established that duplicates the initial trials using a slightly modified set of design variables. The simulation was altered, and the phase one trials were utilized as the first generation of simulated solutions. The objective of this thesis is to explore one method of making energy use and cost estimating more accessible to the construction industry by combining simulation optimization and indexing. The results indicate that this study’s proposed augmented approach has potential benefits to building design optimization, although more research is required to validate this hypothesis in its entirety. This study concludes that the proposed approach can potentially reduce the time needed for individual optimization exercises by creating a cumulative, robust catalog of previous computations that will inform and seed future analyses. The research was conducted in five general stages. The first part defines the research problem and scope of research to be conducted. In the second part, the concepts of genetic algorithms and energy simulation are explored in a comprehensive literature review. The remaining parts explain the trial simulations performed in this study. Part three explains the experiment’s methodology, and part four describes the simulation results. The fifth and final part looks at what the possible conclusions that can be made from analyzing the study’s results. en_US
dc.description.degree MS en_US
dc.identifier.uri http://hdl.handle.net/1853/45952
dc.publisher Georgia Institute of Technology en_US
dc.subject Building optimization en_US
dc.subject Energy modeling en_US
dc.subject Genetic algorithm en_US
dc.subject.lcsh Multidisciplinary design optimization
dc.subject.lcsh Architecture and energy conservation
dc.subject.lcsh Algorithms
dc.subject.lcsh Genetic algorithms
dc.title Testing the impact of using cumulative data with genetic algorithms for the analysis of building energy performance and material cost en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.advisor Ashuri, Baabak
local.contributor.corporatename College of Design
local.contributor.corporatename School of Building Construction
local.relation.ispartofseries Building Construction Graduate Program
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relation.isSeriesOfPublication 1ae3308a-2b56-4c89-8669-a4c987e93f4c
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