Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines
Joint Authors
Zhang, Qian
Yang, Kaihong
Wang, Lihui
Zhou, Siyang
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
At present, many large-scale engineering equipment can obtain massive in-situ data at runtime.
In-depth data mining is conducive to the real-time understanding of equipment operation status or recognition of service environment.
This paper proposes a geological type recognition system by the analysis of in-situ data recorded during TBM tunneling to address geological information acquisition during TBM construction.
Owing to high dimensionality and nonlinear coupling between parameters of TBM in-situ data, the dimensionality reduction feature engineering and machine learning methods are introduced into TBM in-situ data analysis.
The chi-square test is used to screen for sensitive features due to the disobedience to common distributions of TBM parameters.
Considering complex relationships, ANN, SVM, KNN, and CART algorithms are used to construct a geology recognition classifier.
A case study of a subway tunnel project constructed using an earth pressure balance tunnel boring machine (EPB-TBM) in China is used to verify the effectiveness of the proposed geological recognition method.
The result shows that the recognition accuracy gradually increases to a stable level with the increase of input features, and the accuracy of all algorithms is higher than 97%.
Seven features are considered as the best selection strategy among SVM, KNN, and ANN, while feature selection is an inherent part of the CART method which shows a good recognition performance.
This work provides an intelligent path for obtaining geological information for underground excavation TBM projects and a possibility for solving the problem of engineering recognition of more complex geological conditions.
American Psychological Association (APA)
Zhang, Qian& Yang, Kaihong& Wang, Lihui& Zhou, Siyang. 2020. Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1194162
Modern Language Association (MLA)
Zhang, Qian…[et al.]. Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1194162
American Medical Association (AMA)
Zhang, Qian& Yang, Kaihong& Wang, Lihui& Zhou, Siyang. Geological Type Recognition by Machine Learning on In-Situ Data of EPB Tunnel Boring Machines. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1194162
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194162