2022
DOI: 10.1016/j.engstruct.2021.113365
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Application of machine learning methods on real bridge monitoring data

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Cited by 22 publications
(8 citation statements)
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“…[31][32][33] Based on periodic measurements, the long-term crack development can be analyzed. In addition, extrapolation methods, for example, based on machine learning approaches, 34 enable the prediction of crack widths so that maintenance measures can be planned before the crack has a negative impact on the durability of the structure.…”
Section: Potentials Of Fiber Optic Sensing and Aim Of This Studymentioning
confidence: 99%
“…[31][32][33] Based on periodic measurements, the long-term crack development can be analyzed. In addition, extrapolation methods, for example, based on machine learning approaches, 34 enable the prediction of crack widths so that maintenance measures can be planned before the crack has a negative impact on the durability of the structure.…”
Section: Potentials Of Fiber Optic Sensing and Aim Of This Studymentioning
confidence: 99%
“…To roll out the concept to the large number of bridges, the condition assessment needs to be facilitated. In addition to engineering approaches, data-based approaches such as in [19] are promising in this process.…”
Section: Discussionmentioning
confidence: 99%
“…Für einen Strategiewandel hin zu einer prädiktiven Instandhaltung bedarf es einer Vorgehensweise, die Daten zur Erkennung von Anomalien nutzt und somit Schädigungen vor dem Auftreten ankündigt. Neben Hilfsmitteln wie Bauwerksmonitoring oder numerischen Tragwerksanalysen lassen sich Bestandsdaten in Form von Inspektionsdaten auswerten, um neuralgische Bauwerkspunkte zu ermitteln [4, 5]. Eine Methode, die dem Prinzip der FMEA folgt, könnte hierzu einen Beitrag leisten, indem mit ihr kritische Schäden aus den Daten identifiziert werden.…”
Section: Motivation Und Zielstellungunclassified