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http://dx.doi.org/10.12814/jkgss.2020.19.1.011

A study on Development of Artificial Neural Network (ANN) for Preliminary Design of Urban Deep Ex cavation and Tunnelling  

Yoo, Chungsik (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan Univ. Natural Sciences Campus)
Yang, Jaewon (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan Univ. Natural Sciences Campus)
Publication Information
Journal of the Korean Geosynthetics Society / v.19, no.1, 2020 , pp. 11-23 More about this Journal
Abstract
In this paper development artificial neural networks (ANN) for preliminary design and prediction of urban tunnelling and deep excavation-induced ground settlement was presented. In order to form training and validation data sets for the ANN development, field design and measured data were collected for various tunnelling and deep-excavation sites. The field data were then used as a database for the ANN training. The developed ANN was validated against a testing set and the unused field data in terms of statistical parameters such as R2, RMSE, and MAE. The practical use of ANN was demonstrated by applying the developed ANN to hypothetical conditions. It was shown that the developed ANN can be effectively used as a tool for preliminary excavation design and ground settlement prediction for urban excavation problems.
Keywords
Artificial Neural Network; Bigdata; Deep excavation; Tunnelling; Field monitoring;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Kim, C. Y., Bae, G. J., Hong, S. W., Park, C. H., Moon, H. K. and Shin, H. S. (2001), "Neural network based prediction of ground surface settlements due to tunnelling", Computers and Geotechnics, Vol.29, No.6-7, pp.517-547.
2 MIDAS (2010), MIDAS GEOXD user's manual, MIDAS Information Technology Co.
3 Shi, J., Ortigao, J. A. R. and Bai, J. (1998), "Modular neural networks for predicting settlements during tunneling", Journal of Geotechnical and Geoenvironmental Engineering, ASCE, Vol.124, No.5, pp.389-395.   DOI
4 Wang, Z. W., Sampaco, K. L., Fischer, G. R., Kuker, M. S. and Codlewski, P. M. (2000), "Models of predicting surface settlements due to soil ground tunnelling", Proc. of North American Tunnelling, pp.645-652.
5 Xu, Z. H. (2007), Deformation behavior of deep excavations supported by permanent structures in shanghai soft deposit, Ph.D. thesis, Shanghai Jiao Tong Univ.
6 Yoo, C. (2011), "A GIS-ANN Coupled Approach for Soft Ground Improvement Design", International Journal of Geoengineering, Vol.3, No.3, pp.33-39.
7 Yoo, C.S. and Choi, B. S. (2004), "Prediction of deep excavation-induced ground surface movements using artificial neural network", Journal of Korean geotechnical society, Vol.20, No.3, pp.53-65.
8 Yoo, C., Jeon, Y, W. and Choi, B. S. (2006)a, "IT-based tunnelling risk management system (IT-TURISK)-Development and implementation", Tunnelling and Underground Space Technology, Vol.21, No.2, pp.190-202.   DOI
9 Yoo, C. and Kim, J. M. (2007), "Tunneling performance prediction using an integrated GIS and neural network", Computers and Geotechnics, Vol.34, No.1, pp.19-30.   DOI
10 Yoo, C.S., Kim, S.B., Joseph, B. and Han, D.H. (2006)b, "ANN-based prediction on tunnel behavior", Proc. of Korean Geotechnical Conference, Vol.10, pp.777.
11 Yoo, C.S., Kim, S.B. and Yoo, K.H. (2008), "Development of IT-based tunnel design system", Journal of Korean Tunnelling and Underground Space Association, Vol.10, No.2, pp.153-166.
12 Yoo, C.S., Yang, J. W., Qaisar Abbas, Syed Aizaz Haider (2018), "A Study on Development of Artificial Neural Network (ANN) for Deep Excavation Design", Journal of Korean Geosynthetics Society, Vol.17, No.4, pp.199-212.   DOI
13 Yoo, C., Yoo, K. H. and Park, I. J. (2010), "Development and Implementation of Knowledge-based Underground Excavation Design System", International Journal of Geoengineering, Vol.1, No.2, pp.1-10.