Modeling of Tunnel Boring Machine Performance Employing Random Forest Algorithm

Prediction of tunnel boring machine (TBM) performance is still a challenging research subject in engineering geology, geotechnical engineering, and tunnel engineering communities. The longest railway tunnel with approximately 10 km, the Bahce-Nurdagi tunnel, was projected as twin tubes and TBM excav...

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Published inGeotechnical and geological engineering Vol. 41; no. 7; pp. 4205 - 4231
Main Authors Gokceoglu, C., Bal, C., Aladag, C. H.
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.09.2023
Springer Nature B.V
Subjects
Algorithms
Boring machines
Civil Engineering
Dredging
Drilling & boring machinery
Earth and Environmental Science
Earth Sciences
Engineering geology
Excavation
Geology
Geotechnical engineering
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematical models
Modelling
Original Paper
Parameters
Prediction models
Railway tunnels
Terrestrial Pollution
Tubes
Tunnel construction
Tunnels
Waste Management/Waste Technology
Random forest
Tunnel
Rate of penetration
TBM
Geological and geotechnical parameters
Online AccessGet full text
ISSN0960-3182
1573-1529
DOI10.1007/s10706-023-02516-3

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Abstract Prediction of tunnel boring machine (TBM) performance is still a challenging research subject in engineering geology, geotechnical engineering, and tunnel engineering communities. The longest railway tunnel with approximately 10 km, the Bahce-Nurdagi tunnel, was projected as twin tubes and TBM excavation. One of these tubes was successfully completed and the other is under construction. In this study, the geological and geotechnical parameters of the tunnel route and basic TBM parameters were used to predict the TBM performance. For the purpose of the study, a data set including 5334 cases was compiled. The analyses were performed in two phases, the first phase was performed employing only geological and geotechnical parameters while the basic TBM parameters were considered in the second phase analyses. Although the ANN and ANN-fuzzy models yielded acceptable results, the results clearly showed that the random forest algorithm was superior among all other methods for the data used. The results also revealed that the basic TBM parameters should be considered with advanced modeling techniques needed for a successful prediction model for TBM performance.
AbstractList Prediction of tunnel boring machine (TBM) performance is still a challenging research subject in engineering geology, geotechnical engineering, and tunnel engineering communities. The longest railway tunnel with approximately 10 km, the Bahce-Nurdagi tunnel, was projected as twin tubes and TBM excavation. One of these tubes was successfully completed and the other is under construction. In this study, the geological and geotechnical parameters of the tunnel route and basic TBM parameters were used to predict the TBM performance. For the purpose of the study, a data set including 5334 cases was compiled. The analyses were performed in two phases, the first phase was performed employing only geological and geotechnical parameters while the basic TBM parameters were considered in the second phase analyses. Although the ANN and ANN-fuzzy models yielded acceptable results, the results clearly showed that the random forest algorithm was superior among all other methods for the data used. The results also revealed that the basic TBM parameters should be considered with advanced modeling techniques needed for a successful prediction model for TBM performance.
Prediction of tunnel boring machine (TBM) performance is still a challenging research subject in engineering geology, geotechnical engineering, and tunnel engineering communities. The longest railway tunnel with approximately 10 km, the Bahce-Nurdagi tunnel, was projected as twin tubes and TBM excavation. One of these tubes was successfully completed and the other is under construction. In this study, the geological and geotechnical parameters of the tunnel route and basic TBM parameters were used to predict the TBM performance. For the purpose of the study, a data set including 5334 cases was compiled. The analyses were performed in two phases, the first phase was performed employing only geological and geotechnical parameters while the basic TBM parameters were considered in the second phase analyses. Although the ANN and ANN-fuzzy models yielded acceptable results, the results clearly showed that the random forest algorithm was superior among all other methods for the data used. The results also revealed that the basic TBM parameters should be considered with advanced modeling techniques needed for a successful prediction model for TBM performance.
Author Gokceoglu, C.
Bal, C.
Aladag, C. H.
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crossref_primary_10_1007_s00603_024_04210_z
crossref_primary_10_1007_s10706_023_02731_y
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Tunnel
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Geological and geotechnical parameters
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Snippet Prediction of tunnel boring machine (TBM) performance is still a challenging research subject in engineering geology, geotechnical engineering, and tunnel...
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SubjectTerms Algorithms
Boring machines
Civil Engineering
Dredging
Drilling & boring machinery
Earth and Environmental Science
Earth Sciences
Engineering geology
Excavation
Geology
Geotechnical engineering
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Mathematical models
Modelling
Original Paper
Parameters
Prediction models
Railway tunnels
Terrestrial Pollution
Tubes
Tunnel construction
Tunnels
Waste Management/Waste Technology
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Title Modeling of Tunnel Boring Machine Performance Employing Random Forest Algorithm
URI https://link.springer.com/article/10.1007/s10706-023-02516-3
https://www.proquest.com/docview/2842292983
Volume 41
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