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Title:

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

Document type:
Zeitschriftenaufsatz
Author(s):
Fischer, Anne; Beiderwellen Bedrikow, Alexandre; Tommelein, Iris D.; Nübel, Konrad; Fottner, Johannes
Non-TUM Co-author(s):
ja
Cooperation:
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...     »
Keywords:
digital twin in construction; heavy civil engineering equipment; process reference model; discrete-event simulation; deep learning; activity recognition
Intellectual Contribution:
Discipline-based Research
Journal title:
Algorithms
Journal listet in FT50 ranking:
nein
Year:
2023
Journal volume:
16
Year / month:
2023-04
Journal issue:
4
Pages contribution:
24
Covered by:
Scopus; Web of Science
Fulltext / DOI:
doi:10.3390/a16040212
WWW:
https://www.mdpi.com/1999-4893/16/4/212
Publisher:
MDPI
Publisher address:
Basel, Schweiz
Status:
Verlagsversion / published
Submitted:
20.02.2023
Accepted:
11.04.2023
Date of publication:
18.04.2023
Judgement review:
0
Key publication:
Ja
Peer reviewed:
Ja
International:
Ja
Commissioned:
not commissioned
Professional Journal:
Ja
Technology:
Nein
Interdisciplinarity:
Ja
Mission statement:
Communication, Information
Ethics and Sustainability:
Nein
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