NDLI: Study of the influence of geotechnical parameters on the TBM performance in Tehran–Shomal highway project using ANN and SPSS

Content Provider	Springer Nature Link
Author	Torabi, S. R. Shirazi, H. Hajali, H. Monjezi, M.
Copyright Year	2011
Abstract	Alborz twin tunnel along with an exploratory or service tunnel between the two main tunnels, are the longest tunnels section in Tehran–Shomal highway with 6.3 km length. The service tunnel is designed to be used for geological investigations, ventilation, transportation during the construction of main tunnels, water drainage, ground improvement by grouting, and emergency exit. An open tunnel boring machine (TBM) of Wirth Company was used to drive this service tunnel. With regard to the fact that in such mechanized tunneling projects, performance of the TBMs is of the most importance, which affects the economy and timing of the projects; on the other hand, geotechnical conditions of the region play a significant role in this respect, this effect was investigated during this study. In this study, two main elements of the TBM performance including the rate of penetration and utilization factor were investigated using artificial neural network and Statistical Package for Social Sciences. It is shown that geotechnical conditions have considerable effect on the rate of penetration. Whereas, utilization is largely affected by management and non-rock mass-related parameters including delays, wasted times, maintenance, labor, etc. With regard to the available data, four parameters including uniaxial compressive strength (UCS), friction angle, Poisson’s ratio, and cohesion were selected to be studied. Based on assessments conducted using these approaches, the rate of effectiveness of four selected parameters on penetration rate, in a descending order, was as follows: UCS, friction angle, Poisson’s ratio, and cohesion. For increasing utilization, it was concluded that minimizing time delays by good management is the most effective way. Furthermore, with regard to the relative error percentages and the coefficient of correlation of the input and output data, it was concluded that the method artificial neural network yields more reliable results than the statistical approach.
Starting Page	1215
Ending Page	1227
Page Count	13
File Format	PDF
ISSN	18667511
Journal	Arabian Journal of Geosciences
Volume Number	6
Issue Number	4
e-ISSN	18667538
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-10-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Geotechnical parameters Penetration rate Utilization factor Artificial neural network SPSS Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Environmental Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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