@inproceedings{597122083bf3472fbc8636a0905307f7,
title = "Prediction of ground settlement during tunnelling in water bearing ground",
abstract = "This paper presents an artificial neural network approach for prediction of tunnelling-induced ground groundwater drawdown and associated settlement during tunneling in water bearing ground. A parametric study using a calibrated 2D stress-pore pressure coupled finite element analysis was conducted to form a database concerning the settlement and groundwater drawdown during tunneling in groundwater drawdown environment. An artificial neural network (ANN) was then developed based on the database to establish underlying relationships between tunneling and its consequence. The developed ANN exhibited excellent performance in predicting the magnitude of settlement and groundwater drawdown for tunneling conditions considered in this study, confirming that a generalized ANN can be deployed in practical use. Also examined the relative importance of influencing factors on the settlement and groundwater drawdown during tunneling based on the ANN results.",
keywords = "Artificial neural network, Finite element anlaysis, Groundwater drawdown, Settlement, Tunnelling",
author = "C. Yoo and Kim, \{S. B.\} and Jung, \{H. S.\}",
year = "2009",
doi = "10.3233/978-1-60750-031-5-1953",
language = "English",
isbn = "9781607500315",
series = "Proceedings of the 17th International Conference on Soil Mechanics and Geotechnical Engineering: The Academia and Practice of Geotechnical Engineering",
pages = "1953--1956",
booktitle = "Proceedings of the 17th International Conference on Soil Mechanics and Geotechnical Engineering",
note = "17th International Conference on Soil Mechanics and Geotechnical Engineering, ICSMGE 2009 ; Conference date: 05-10-2009 Through 09-10-2009",
}