Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project Data
The planning of production, logistics, and maintenance in mechanized tunneling relies on project-specific conditions and assumptions about numerous production parameters. Analyzing multiple scenarios for TBM production is crucial for ensuring robust project flow. Process simulation models are valuab...
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/4/1969 |
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| author | Annika Jodehl Judith Berns Markus Thewes Markus König |
| author_facet | Annika Jodehl Judith Berns Markus Thewes Markus König |
| author_sort | Annika Jodehl |
| collection | DOAJ |
| description | The planning of production, logistics, and maintenance in mechanized tunneling relies on project-specific conditions and assumptions about numerous production parameters. Analyzing multiple scenarios for TBM production is crucial for ensuring robust project flow. Process simulation models are valuable for this analysis. However, deviations from the plan often occur due to uncertainties in initial assumptions and unforeseen events during construction, especially given the variable ground conditions in TBM tunneling. This paper presents a concept for adapting offline simulation models used in the planning phase for real-time optimization by integrating actual project data. Continuously updating these models with new data from the construction site improves performance predictions. The concept involves integrating current construction progress into the simulation model and updating the originally assumed input data with project-specific actual data. An example study demonstrates the continuous adjustment of a simulation model and its impact on project duration prognosis. The enhancement of the prognosis by integrating real-time data is investigated, and an initial assessment of the earliest possible time for an update is made. The benefits of using real-time data in process simulation during construction are discussed, highlighting the improved accuracy in performance prediction and the potential for more efficient project management. |
| format | Article |
| id | doaj-art-2113f0302fb64d869910affbb9447063 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2113f0302fb64d869910affbb94470632025-08-20T03:12:20ZengMDPI AGApplied Sciences2076-34172025-02-01154196910.3390/app15041969Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project DataAnnika Jodehl0Judith Berns1Markus Thewes2Markus König3BUNG-PEB Tunnelbau-Ingenieure GmbH, Stockumer Straße 475, 44227 Dortmund, GermanyInstitute for Tunnelling and Construction Management, Ruhr-Universität Bochum, Universitätsstraße 150, 44801 Bochum, GermanyInstitute for Tunnelling and Construction Management, Ruhr-Universität Bochum, Universitätsstraße 150, 44801 Bochum, GermanyChair of Computing in Engineering, Ruhr-Universität Bochum, Universitätsstraße 150, 44801 Bochum, GermanyThe planning of production, logistics, and maintenance in mechanized tunneling relies on project-specific conditions and assumptions about numerous production parameters. Analyzing multiple scenarios for TBM production is crucial for ensuring robust project flow. Process simulation models are valuable for this analysis. However, deviations from the plan often occur due to uncertainties in initial assumptions and unforeseen events during construction, especially given the variable ground conditions in TBM tunneling. This paper presents a concept for adapting offline simulation models used in the planning phase for real-time optimization by integrating actual project data. Continuously updating these models with new data from the construction site improves performance predictions. The concept involves integrating current construction progress into the simulation model and updating the originally assumed input data with project-specific actual data. An example study demonstrates the continuous adjustment of a simulation model and its impact on project duration prognosis. The enhancement of the prognosis by integrating real-time data is investigated, and an initial assessment of the earliest possible time for an update is made. The benefits of using real-time data in process simulation during construction are discussed, highlighting the improved accuracy in performance prediction and the potential for more efficient project management.https://www.mdpi.com/2076-3417/15/4/1969mechanized tunnelingprocess simulationperformance prognosisreal-time simulation |
| spellingShingle | Annika Jodehl Judith Berns Markus Thewes Markus König Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project Data Applied Sciences mechanized tunneling process simulation performance prognosis real-time simulation |
| title | Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project Data |
| title_full | Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project Data |
| title_fullStr | Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project Data |
| title_full_unstemmed | Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project Data |
| title_short | Improving the Performance Prediction of Process Simulation Models for TBM Tunneling Using Real-Time Project Data |
| title_sort | improving the performance prediction of process simulation models for tbm tunneling using real time project data |
| topic | mechanized tunneling process simulation performance prognosis real-time simulation |
| url | https://www.mdpi.com/2076-3417/15/4/1969 |
| work_keys_str_mv | AT annikajodehl improvingtheperformancepredictionofprocesssimulationmodelsfortbmtunnelingusingrealtimeprojectdata AT judithberns improvingtheperformancepredictionofprocesssimulationmodelsfortbmtunnelingusingrealtimeprojectdata AT markusthewes improvingtheperformancepredictionofprocesssimulationmodelsfortbmtunnelingusingrealtimeprojectdata AT markuskonig improvingtheperformancepredictionofprocesssimulationmodelsfortbmtunnelingusingrealtimeprojectdata |