Prognose von Messdaten beim Bauwerksmonitoring mithilfe von Machine Learning

Abstract

Prediction of the bridge temperature using monitoring data and machine learning In this paper, the nonlinear or rather transient relationship between the air temperature and the building temperature is simulated by a machine learning model. Based on this modelling, different use cases for the application of machine learning regression methods to monitoring data are presented, which resulted from practical questions. Basic knowledge of neural networks will be given and for the calculations, long-term monitoring data of valley bridges from VDE 8 are used. It is shown, for example, that these methods can be used to detect and compensate measurement errors or to predict the behaviour of structures. The results show that these methods, have a great potential for the evaluation of large amounts of data since no physical models are required. The paper focuses more on the application of AI methods rather than on the mathematical or theoretical background of the methods.

Crack monitoring on concrete structures: Comparison of various distributed fiber optic sensors with digital image correlation method

“…To allow existing structures to be used for a prolonged period of time while maintaining the same level of safety, the concept of the digital twin 2–4 gains popularity in the construction industry. In digital twins, structural health monitoring (SHM) 5–8 serves as the most important link between the real object and its virtual representation, allowing structures to be monitored in near real time and also a prediction of their condition can be made 9,10 . Measurement errors 11 and influences from aging measurement systems 12 can negatively affect the quality of the monitoring data and must therefore be corrected automatically.…”

“…In digital twins, structural health monitoring (SHM) [5][6][7][8] serves as the most important link between the real object and its virtual representation, allowing structures to be monitored in near real time and also a prediction of their condition can be made. 9,10 Measurement errors 11 and influences from aging measurement systems 12 can negatively affect the quality of the monitoring data and must therefore be corrected automatically.…”

Digitalisierung der Versuchsplanung im Ingenieurbau – Planung von Messprojekten mit MR‐Brillen, 3‐D‐Druck und QR‐Codes für Monitoring und datengestützte Bestandsbewertung

“…Bedingt durch die Alterung der Verkehrsinfrastruktur und die gleichzeitig steigenden Verkehrslasten gewinnen Methoden der datengestützten Bauwerksbewertung und des Monitorings an Bedeutung [12,13]. Auf die Möglichkeiten der umfassenden Modellierung aller Systemkomponenten wird in diesem Beitrag nicht näher eingegangen, sondern stattdessen auf [11] verwie sen, wo die Generierung eines AsbuiltModells der hier betrachteten Brücke beschrieben wird.…”