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Parametric design and optimization of pipe bridges: Automatizing of design process and cost optimization
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Structural Engineering and Bridges.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recent advances in computer intelligence have led to the development of a number of optimization algorithms, used to solve complex problems. They have the potential to find optimal or near-optimal solutions in a reasonable time frame. Structural optimization is a field where such algorithms are used to optimize the design of structures. Despite the great amount of published research, the practice does not reflect that progress as the results usually concern small or unrealistic problems. Simultaneously, tools for automating tasks in the design process are being developed to help the designer in repetitive and complex actions. Made to be easily usable, they are today fully implemented in largely used softwares and are thus ready to become an everyday tool for designers. Designs based on a limited number of parameters are also especially adapted for an optimization process to be implemented. Combining parametric design and optimization has great potential in the development of the design practice among engineers and this thesis is addressing the need for a practical usage of optimization by presenting a software application for automated design and optimization of pipe bridges. Used as a preliminary design, limited in its details and complex layout, it shows the opportunities of using computational methods in structural engineering. This thesis introduces the concepts of parametric design and optimization in structural engineering practice, explains how the software application works and presents a case study carried out to evaluate the results. The results of the case study suggest a great potential in cost saving. Yet, the speeding up of the process and the opportunity of working on an adaptable design is perhaps the greatest benefit that would make designers turn to such practice. These findings show that the current tools for parametric design and optimization are robust enough and ready to use in order to extend the design practice and obtain better solutions.

Place, publisher, year, edition, pages
2019.
Series
TRITA-ABE-MBT ; 19439
Keywords [en]
Parametric design, Optimization, Pipe bridge, Metaheuristic algorithm, Genetic algorithm, Particle swarm optimization, Simulated annealing, Visual programming, Grasshopper
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-254340OAI: oai:DiVA.org:kth-254340DiVA, id: diva2:1330951
Available from: 2019-06-26 Created: 2019-06-26

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf