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dc.contributor.authorRonghin Hsu
dc.contributor.authorHsu-Chih Lee
dc.contributor.authorSzu-Pyng Kao
dc.date.accessioned2017-05-08T21:01:49Z
dc.date.available2017-05-08T21:01:49Z
dc.date.copyrightMay 2008
dc.date.issued2008
dc.identifier.other%28asce%290733-9453%282008%29134%3A2%2861%29.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl/handle/yetl/36016
dc.description.abstractAfter least squares adjustment, the possible outliers among the observations are detected using a statistical test, such as Baarda’s data-snooping test. However, statistical tests do not guarantee that all of the incorrect observations will be detected. Robustness analysis is a technique that augments this classical approach with geometrical strength analysis using strain. The technique allows the effect of undetected errors among the observations to be portrayed. In this technical note, we will show that three-dimensional networks are usually horizontally superior in robustness by explaining mathematically why the magnitudes of the deformation measures in the
dc.publisherAmerican Society of Civil Engineers
dc.titleThree-Dimensional Networks Are Horizontally Superior in Robustness: A Mathematical Reasoning
dc.typeJournal Paper
dc.journal.volume134
dc.journal.issue2
dc.journal.titleJournal of Surveying Engineering
dc.identifier.doi10.1061/(ASCE)0733-9453(2008)134:2(61)
dc.treeJournal of Surveying Engineering:;2008:;Volume ( 134 ):;issue: 002
dc.contenttypeFulltext


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