Structural optimization applying neural networks
Structural optimization applying neural networks
Type of document
disertační práceAuthor
Müller Jacob
Supervisor
Křístek Vladimír
Opponent
Procházka Petr
Field of study
Konstrukce a dopravní stavbyStudy program
Stavební inženýrstvíInstitutions assigning rank
Fakulta stavebníDefended
2013-05-22 00:00:00.0Rights
A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one’s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://www.cvut.cz/sites/default/files/content/d1dc93cd-5894-4521-b799-c7e715d3c59e/cs/20160901-metodicky-pokyn-c-12009-o-dodrzovani-etickych-principu-pri-priprave-vysokoskolskych.pdfVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://www.cvut.cz/sites/default/files/content/d1dc93cd-5894-4521-b799-c7e715d3c59e/cs/20160901-metodicky-pokyn-c-12009-o-dodrzovani-etickych-principu-pri-priprave-vysokoskolskych.pdf
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Structures are designed to fulfill requirements established in numerous design codes or standards. After the design and construction phase is completed, only a few - sophisticated or important structures, such as bridges - are monitored. However, these monitoring systems are mostly non permanent and typically measure only one or two parameters, which leads to incomplete or questionable results. The thesis is focused on the development of an "intelligent monitoring system". This is achieved by applying neural network algorithms to analyze several complex parameters. This approach achieves reliable results, because the neural network is capable to detect an unusual condition through a self learning process. Structures are designed to fulfill requirements established in numerous design codes or standards. After the design and construction phase is completed, only a few - sophisticated or important structures, such as bridges - are monitored. However, these monitoring systems are mostly non permanent and typically measure only one or two parameters, which leads to incomplete or questionable results. The thesis is focused on the development of an "intelligent monitoring system". This is achieved by applying neural network algorithms to analyze several complex parameters. This approach achieves reliable results, because the neural network is capable to detect an unusual condition through a self learning process.
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