Abstract
With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation. This research aims at developing a regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR). For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. In order to find out the optimum values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. According to the results, it can be said that the proposed model is a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists. From the prediction results of training and testing samples, the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.99 and 0.95, respectively, which shows a high conformity between predicted and actual penetration rate.
| Original language | English |
|---|---|
| Pages (from-to) | 214-229 |
| Number of pages | 16 |
| Journal | International Journal of Rock Mechanics and Mining Sciences |
| Volume | 72 |
| DOIs | |
| Publication status | Published - Dec 1 2014 |
| Externally published | Yes |
Fingerprint
Keywords
- Penetration rate
- Queens water tunnel
- SVR
- TBM performance
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
Cite this
A support vector regression model for predicting tunnel boring machine penetration rates. / Mahdevari, Satar; Shahriar, Kourosh; Yagiz, Saffet; Akbarpour Shirazi, Mohsen.
In: International Journal of Rock Mechanics and Mining Sciences, Vol. 72, 01.12.2014, p. 214-229.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A support vector regression model for predicting tunnel boring machine penetration rates
AU - Mahdevari, Satar
AU - Shahriar, Kourosh
AU - Yagiz, Saffet
AU - Akbarpour Shirazi, Mohsen
PY - 2014/12/1
Y1 - 2014/12/1
N2 - With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation. This research aims at developing a regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR). For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. In order to find out the optimum values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. According to the results, it can be said that the proposed model is a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists. From the prediction results of training and testing samples, the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.99 and 0.95, respectively, which shows a high conformity between predicted and actual penetration rate.
AB - With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation. This research aims at developing a regression model to predict penetration rate of TBM in hard rock conditions based on a new artificial intelligence (AI) algorithm namely support vector regression (SVR). For this purpose, the Queens Water Tunnel, in New York City, was selected as a case study to test the proposed model. In order to find out the optimum values of the parameters and prevent over-fitting, 80% of the total data were selected randomly for training set and the rest were kept for testing the model. According to the results, it can be said that the proposed model is a useful and reliable means to predict TBM penetration rate provided that a suitable dataset exists. From the prediction results of training and testing samples, the squared correlation coefficient (R2) between the observed and predicted values of the proposed model was obtained 0.99 and 0.95, respectively, which shows a high conformity between predicted and actual penetration rate.
KW - Penetration rate
KW - Queens water tunnel
KW - SVR
KW - TBM performance
UR - http://www.scopus.com/inward/record.url?scp=84949115475&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949115475&partnerID=8YFLogxK
U2 - 10.1016/j.ijrmms.2014.09.012
DO - 10.1016/j.ijrmms.2014.09.012
M3 - Article
VL - 72
SP - 214
EP - 229
JO - International Journal of Rock Mechanics and Minings Sciences
JF - International Journal of Rock Mechanics and Minings Sciences
SN - 1365-1609
ER -