Predicting Tunnel Squeezing Using Multiclass Support Vector Machines
Joint Authors
Sun, Yang
Feng, Xianda
Yang, Lingqiang
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-16
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses.
It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties.
Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels.
This study presents a modified application of a multiclass support vector machine (SVM) to predict tunnel squeezing based on four parameters, that is, diameter (D), buried depth (H), support stiffness (K), and rock tunneling quality index (Q).
We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model.
The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%.
Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy.
More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.
American Psychological Association (APA)
Sun, Yang& Feng, Xianda& Yang, Lingqiang. 2018. Predicting Tunnel Squeezing Using Multiclass Support Vector Machines. Advances in Civil Engineering،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1115993
Modern Language Association (MLA)
Sun, Yang…[et al.]. Predicting Tunnel Squeezing Using Multiclass Support Vector Machines. Advances in Civil Engineering No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1115993
American Medical Association (AMA)
Sun, Yang& Feng, Xianda& Yang, Lingqiang. Predicting Tunnel Squeezing Using Multiclass Support Vector Machines. Advances in Civil Engineering. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1115993
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1115993