Deep learning-based prediction of particle size distributions in construction and demolition waste recycling using convolutional neural networks on 3D laser triangulation data

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: 10.1016/j.conbuildmat.2025.140214

Event: Veröffentlichung

(where): Aachen

(who): Universitätsbibliothek der RWTH Aachen

(when): 2025

Creator: Kroell, Nils
Thor, Eric
Göbbels, Lieve
Schönfelder, Paula Valentina
Chen, Xiaozheng

DOI: 10.18154/RWTH-2025-01896

URN: urn:nbn:de:101:1-2503060044314.733343980820

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:19 AM CEST

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Associated

Kroell, Nils
Thor, Eric
Göbbels, Lieve
Schönfelder, Paula Valentina
Chen, Xiaozheng
Universitätsbibliothek der RWTH Aachen

Time of origin

2025

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Deep learning-based prediction of particle size distributions in construction and demolition waste recycling using convolutional neural networks on 3D laser triangulation data

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Other Objects (12)

Deep learning-based instance segmentation on 3D laser triangulation data for inline monitoring of particle size distributions in construction and demolition waste recycling

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Deep learning-based instance segmentation on 3D laser triangulation data for inline monitoring of particle size distributions in construction and demolition waste recycling

Environmental feasibility of recycling construction and demolition waste

DEMOLITION SIEGESALLEE;MONUMENTS, DEMOLITION SIEGESAL

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