Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks
Joint Authors
Lai, Jinxing
Qiu, Junling
Fan, Haobo
Chen, Jianxun
Feng, Zhihua
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-12-24
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass.
Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models.
Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation.
Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods.
In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction.
And the application cases as well as the improvement of ANN models were also presented.
The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability.
American Psychological Association (APA)
Lai, Jinxing& Qiu, Junling& Feng, Zhihua& Chen, Jianxun& Fan, Haobo. 2015. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099736
Modern Language Association (MLA)
Lai, Jinxing…[et al.]. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-16.
https://search.emarefa.net/detail/BIM-1099736
American Medical Association (AMA)
Lai, Jinxing& Qiu, Junling& Feng, Zhihua& Chen, Jianxun& Fan, Haobo. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks. Computational Intelligence and Neuroscience. 2015. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1099736
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099736