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Grundlagen zur automatisierten Baufortschrittsüberwachung mittels Deep Learning basierend auf Punktwolken und Bauinformationsmodellen

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Zusammenfassung

In diesem Kapitel werden die theoretischen Grundlagen für die spätere Entwicklung und Umsetzung einer vollautomatischen, Deep Learning unterstützten Prozesskette beschrieben, die den Baufortschritt durch den Vergleich eines Bauinformationsmodells mit den Punktwolken der Baustelle ermitteln kann. Dazu werden digitale Baustellenabbildungen in Form von 3D-Punktwolken betrachtet und deren Eigenschaften beschrieben. Darüber hinaus werden die theoretischen Grundlagen zur Verarbeitung dieser Punktwolkenund deren Registrierung zum Soll-Ist-Vergleich erläutert. Weiterhin werden verschiedene Datenverarbeitungsmethoden diskutiert, die das Ziel haben, Bauelemente in den Punktwolken zu erkennen. Dabei werden sowohl numerische Methoden als auch Deep-Learning-Ansätze zur Datenanalyse und Klassifikation zusammengefasst und analysiert.

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Fahrendholz, J.L., Brell-Cokcan, S. (2024). Grundlagen zur automatisierten Baufortschrittsüberwachung mittels Deep Learning basierend auf Punktwolken und Bauinformationsmodellen. In: Brell-Cokcan, S., Schmitt, R.H. (eds) IoC - Internet of Construction . Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-42544-9_21

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