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Details

Autor(en) / Beteiligte
Titel
Passive seismic monitoring in conventional tunnelling – An innovative approach for automatic process recognition using support vector machines
Ist Teil von
  • Tunnelling and underground space technology, 2023-07, Vol.137, p.105149, Article 105149
Ort / Verlag
Elsevier Ltd
Erscheinungsjahr
2023
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
  • •Geophone signals are assigned to tunnel construction processes using machine learning.•Successive processes of NATM are classified using a SVM algorithm.•Automatic creation of working diagrams through passive tunnel construction monitoring.•Predictive maintenance through machine operation times recording. For the underground construction sector as in conventional tunnelling, there is still a lack of automatization and digitalization progresses, especially concerning tunnel construction monitoring. A manual documentation of the time intervals for subsequent processes by the respective employee is currently state of the art. This study addresses a cost and time effective data acquisition and evaluation method using conventional geophones for the differentiation of the processes involved in tunnel construction by analysis of elastic wave signals. The field experiments were executed at the construction site of “Zentrum am Berg” in Austria where seismic signals were recorded during the conventional tunnel excavation process. The seismic emissions induced by the respective machinery during different constructuon steps are distinguished with a machine learning approach using support vector machines, leading to the possibility of associating them with the corresponding time of the machinery in use. The semi-automatic evaluation of the gathered data should facilitate the documentation of the daily working diagrams, supplement project management and effective planning and optimize predictive maintenance possibilities in the underground construction industry.
Sprache
Englisch
Identifikatoren
ISSN: 0886-7798
eISSN: 1878-4364
DOI: 10.1016/j.tust.2023.105149
Titel-ID: cdi_crossref_primary_10_1016_j_tust_2023_105149

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