Paper Info
Title
Performance prediction of tunnel boring machine through developing a gene expression programming equation.
Abstract
The prediction of tunnel boring machine (TBM) performance in a specified rock mass condition is crucial for any mechanical tunneling project. TBM performance prediction in accurate may reduce the risks related to high capital costs and scheduling for tunneling. This paper presents a new model/equation based on gene expression programming (GEP) to estimate performance of TBM by means of the penetration rate (PR). To achieve the aim of the study, the Pahang–Selangor Raw Water Transfer tunnel in Malaysia was investigated and the data related to field observations and laboratory tests were used in modelling of PR of TBM. A database (1286 datasets in total) comprising 7 model inputs related to rock (mass and material) properties and machine characteristics and 1 output (PR) was prepared to use in GEP modelling. To evaluate capability of the developed GEP equation, a multiple regression (MR) model was also proposed. A comparison between the obtained results has been done using several performance indices and the best equations of GEP and MR were selected. System results for the developed GEP equation based on coefficient of determination (R 2) were obtained as 0.855 and 0.829 for training and testing datasets, respectively, while these values were achieved as 0.795 and 0.789 for the developed MR equation. Concluding remark is that the GEP equation is superior in comparison with the MR equation and it can be introduced as a new equation in the field of TBM performance prediction.
Year
DOI
Venue
2018
10.1007/s00366-017-0526-x
Eng. Comput. (Lond.)
Keywords
Field
DocType
Tunnel boring machine, Penetration rate, Gene expression programming, Multiple regression
Gene expression programming,Mathematical optimization,Rock mass classification,Simulation,Scheduling (computing),Algorithm,Expression (computer science),Coefficient of determination,Performance prediction,Tunnel boring machine,Mathematics,Linear regression
Journal
Volume
Issue
ISSN
34
1
0177-0667
Citations 
PageRank 
References 
4
0.50
14
Authors
5
Name
Order
Citations
PageRank
danial jahed armaghani158536.46
roohollah shirani faradonbeh21409.21
Ehsan Momeni3181.48
Fahimifar, A.4142.14
Mahmood M. D. Tahir51029.56