Synchronized Load Quantification from Multiple Data Records for Analysing High-rise Buildings

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Behrendt, M.; Punurai, W.; Beer, M.: Synchronized Load Quantification from Multiple Data Records for Analysing High-rise Buildings. 7th Asia Conference on Earthquake Engineering, 22-25 November 2018, Bangkok, Thailand. http://acee2018.org/

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/4957

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Sum total of downloads: 183




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Abstract: 
To analyse the reliability and durability of large complex structures such as high-rise buildings, most realistically, it is advisable to utilize site-specific load characteristics. Such load characteristics can be made available as data records, e.g. representing measured wind or earthquake loads. Due to various circumstances such as measurement errors, equipment failures, or sensor limitations, the data records underlie uncertainties. Since these uncertainties affect the results of the simulation of complex structures, they must be mitigated as much as possible. In this work, the Procrustes analysis, finding similarity transformations between two sets of points in n-dimensional space is used and is extended to uncertainties so that data records can be analysed regarding the uncertainty. To find the best matching of two sets of points the Kabsch algorithm is used. In this manner, a basis is created to simulate and assess the reliability of high-rise buildings under load due to wind and earthquakes.
License of this version: Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
Document Type: ConferenceObject
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 101 55.19%
2 image of flag of Thailand Thailand 18 9.84%
3 image of flag of United States United States 17 9.29%
4 image of flag of United Kingdom United Kingdom 9 4.92%
5 image of flag of China China 4 2.19%
6 image of flag of Ireland Ireland 3 1.64%
7 image of flag of Russian Federation Russian Federation 2 1.09%
8 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 2 1.09%
9 image of flag of India India 2 1.09%
10 image of flag of Chile Chile 2 1.09%
    other countries 23 12.57%

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