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Titel:

From activity recognition to simulation: impact of granularity on production models in heavy civil engineering

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Fischer, Anne; Beiderwellen Bedrikow, Alexandre; Tommelein, Iris D.; Nübel, Konrad; Fottner, Johannes
Nicht-TUM Koautoren:
ja
Kooperation:
international
Abstract:
As in manufacturing with its Industry 4.0 transformation, the enormous potential of artificial intelligence (AI) is also being recognized in the construction industry. Specifically, the equipment-intensive construction industry can benefit from using AI. AI applications can leverage the data recorded by the numerous sensors on machines and mirror them in a digital twin. Analyzing the digital twin can help optimize processes on the construction site and increase productivity. We present a case fr...     »
Stichworte:
digital twin in construction; heavy civil engineering equipment; process reference model; discrete-event simulation; deep learning; activity recognition
Intellectual Contribution:
Discipline-based Research
Zeitschriftentitel:
Algorithms
Journal gelistet in FT50 Ranking:
nein
Jahr:
2023
Band / Volume:
16
Jahr / Monat:
2023-04
Heft / Issue:
4
Seitenangaben Beitrag:
24
Nachgewiesen in:
Scopus; Web of Science
Volltext / DOI:
doi:10.3390/a16040212
WWW:
https://www.mdpi.com/1999-4893/16/4/212
Verlag / Institution:
MDPI
Verlagsort:
Basel, Schweiz
Status:
Verlagsversion / published
Eingereicht (bei Zeitschrift):
20.02.2023
Angenommen (von Zeitschrift):
11.04.2023
Publikationsdatum:
18.04.2023
Urteilsbesprechung:
0
Key publication:
Ja
Peer reviewed:
Ja
International:
Ja
commissioned:
not commissioned
Professional Journal:
Ja
Technology:
Nein
Interdisziplinarität:
Ja
Leitbild:
Communication, Information
Ethics und Sustainability:
Nein
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