Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3
Abstract Accurately predicting the performance of Earth Pressure Balance Tunnel Boring Machines (EPB-TBMs) in soft ground conditions is crucial yet challenging due to the complex interaction of geological and operational factors. This study investigates Mashhad Metro Line 3, where a TBM was employed...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | International Journal of Geo-Engineering |
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| Online Access: | https://doi.org/10.1186/s40703-024-00228-y |
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| author | Morteza Abbasi Amir Hossein Namadchi Mehdi Abbasi Mohsen Abbasi |
| author_facet | Morteza Abbasi Amir Hossein Namadchi Mehdi Abbasi Mohsen Abbasi |
| author_sort | Morteza Abbasi |
| collection | DOAJ |
| description | Abstract Accurately predicting the performance of Earth Pressure Balance Tunnel Boring Machines (EPB-TBMs) in soft ground conditions is crucial yet challenging due to the complex interaction of geological and operational factors. This study investigates Mashhad Metro Line 3, where a TBM was employed to excavate a 1831-m section through variable soil compositions, including significant cobble and boulder content, presenting unique challenges to performance prediction. To address these complexities, several machine learning models—Multiple Linear Regression (MLR), Decision Trees (DT), and Multi-Layer Perceptron (MLP) neural networks—were applied to predict TBM penetration rates and assess model efficacy. Beginning with a dataset of 438,960 rows, rigorous feature selection and data processing yielded a final dataset of 1833 rows. Among the models, MLR achieved an R2 score of 0.991, closely matching the more complex MLP model, which reached an R2 score of 0.988. In contrast, the Decision Tree model demonstrated a lower R2 score of 0.923, suggesting a tendency to overfit. While MLR provided an effective, straightforward approach, MLP proved valuable for capturing non-linear patterns that could improve predictive accuracy in more variable tunneling conditions. These findings underscore the practical applications of both simple and complex machine learning models in enhancing TBM performance prediction. |
| format | Article |
| id | doaj-art-2954ba08a07741a2b8fc02e4532adff8 |
| institution | Kabale University |
| issn | 2198-2783 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Geo-Engineering |
| spelling | doaj-art-2954ba08a07741a2b8fc02e4532adff82025-01-05T12:07:24ZengSpringerOpenInternational Journal of Geo-Engineering2198-27832024-12-0115112410.1186/s40703-024-00228-yEvaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3Morteza Abbasi0Amir Hossein Namadchi1Mehdi Abbasi2Mohsen Abbasi3Department of Civil Engineering, Mashhad Branch, Islamic Azad UniversityDepartment of Civil Engineering, Eqbal Lahoori Institute of Higher EducationDepartment of Geology, Faculty of Science, Ferdowsi UniversityTehran Science and Research Branch, Islamic Azad UniversityAbstract Accurately predicting the performance of Earth Pressure Balance Tunnel Boring Machines (EPB-TBMs) in soft ground conditions is crucial yet challenging due to the complex interaction of geological and operational factors. This study investigates Mashhad Metro Line 3, where a TBM was employed to excavate a 1831-m section through variable soil compositions, including significant cobble and boulder content, presenting unique challenges to performance prediction. To address these complexities, several machine learning models—Multiple Linear Regression (MLR), Decision Trees (DT), and Multi-Layer Perceptron (MLP) neural networks—were applied to predict TBM penetration rates and assess model efficacy. Beginning with a dataset of 438,960 rows, rigorous feature selection and data processing yielded a final dataset of 1833 rows. Among the models, MLR achieved an R2 score of 0.991, closely matching the more complex MLP model, which reached an R2 score of 0.988. In contrast, the Decision Tree model demonstrated a lower R2 score of 0.923, suggesting a tendency to overfit. While MLR provided an effective, straightforward approach, MLP proved valuable for capturing non-linear patterns that could improve predictive accuracy in more variable tunneling conditions. These findings underscore the practical applications of both simple and complex machine learning models in enhancing TBM performance prediction.https://doi.org/10.1186/s40703-024-00228-yTunnel boring machine (TBM)Machine learning modelsData preprocessingPenetration rate predictionFeature selectionDecision trees |
| spellingShingle | Morteza Abbasi Amir Hossein Namadchi Mehdi Abbasi Mohsen Abbasi Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 International Journal of Geo-Engineering Tunnel boring machine (TBM) Machine learning models Data preprocessing Penetration rate prediction Feature selection Decision trees |
| title | Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 |
| title_full | Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 |
| title_fullStr | Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 |
| title_full_unstemmed | Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 |
| title_short | Evaluation of machine learning algorithms in tunnel boring machine applications: a case study in Mashhad metro line 3 |
| title_sort | evaluation of machine learning algorithms in tunnel boring machine applications a case study in mashhad metro line 3 |
| topic | Tunnel boring machine (TBM) Machine learning models Data preprocessing Penetration rate prediction Feature selection Decision trees |
| url | https://doi.org/10.1186/s40703-024-00228-y |
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