Measurement and Enhancing Prediction of EPBM Torque using Actual Machine Data

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aut.relation.articlenumber113780
aut.relation.endpage113780
aut.relation.journalMeasurement
aut.relation.startpage113780
dc.contributor.authorKoohsari, Ali
dc.contributor.authorKalatehjari, Roohollah
dc.contributor.authorMoosazadeh, Sayfoddin
dc.contributor.authorHajihassani, Mohsen
dc.contributor.authorTarafrava, Mostafa
dc.date.accessioned2023-11-09T04:17:08Z
dc.date.available2023-11-09T04:17:08Z
dc.date.issued2023-11
dc.description.abstractThe cutterhead torque of an Earth Pressure Balance Machine (EPBM) plays a critical role in determining the performance of mechanized tunneling in urban areas. However, as this parameter is not directly set by the operator but is a function of geological conditions, thrust force, screw conveyor revolution speed, and soil conditions, it is closely linked to geotechnical parameters and machine settings. Despite previous attempts to predict EPBM torque using Shi's physical model, accuracy has been lacking. This study aims to improve the accuracy of this prediction by utilizing actual data from an EPBM used in a metro line tunneling project to identify the primary factors influencing torque and to modify related equations accordingly. To evaluate the performance of existing models and the predictions made by the presented method, various metrics are utilized, including the correlation between all torque values, the relationship between torque and thrust values, and the connection between thrust pressure and penetration. The results indicate that in addition to geotechnical parameters, machine settings such as thrust force, cutterhead revolution speed, arching pressure, soil conditions, and chamber pressure significantly impact the torque value. The study found that the thrust force exerted by the EPBM is a key factor influencing torque.
dc.identifier.citationMeasurement, ISSN: 0263-2241 (Print), Elsevier BV, 113780-113780. doi: 10.1016/j.measurement.2023.113780
dc.identifier.doi10.1016/j.measurement.2023.113780
dc.identifier.issn0263-2241
dc.identifier.urihttp://hdl.handle.net/10292/16910
dc.languageen
dc.publisherElsevier BV
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0263224123013441
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject46 Information and Computing Sciences
dc.subject49 Mathematical Sciences
dc.subject0102 Applied Mathematics
dc.subject0801 Artificial Intelligence and Image Processing
dc.subject0913 Mechanical Engineering
dc.subjectElectrical & Electronic Engineering
dc.subject46 Information and computing sciences
dc.subject49 Mathematical sciences
dc.titleMeasurement and Enhancing Prediction of EPBM Torque using Actual Machine Data
dc.typeJournal Article
pubs.elements-id528710
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