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Bulletin of Engineering Geology and the Environment

Erschienen in:

18.11.2016 | Original Paper

TBM performance estimation using a classification and regression tree (CART) technique

verfasst von: Alireza Salimi, Roohollah Shirani Faradonbeh, Masoud Monjezi, Christian Moormann

Erschienen in: Bulletin of Engineering Geology and the Environment | Ausgabe 1/2018

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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 the advance rate of an excavation. In this study, a database of actual machine performance from T05 and T06 tunnels of the deep tunnel sewerage system (DTSS) project in Singapore which include: rock mass uniaxial compressive strength, brittleness index (B _i), volumetric joint account (J _v), joint orientation (J _o), TBM specifications and corresponding TBM performance has been compiled. Then, for prediction of specific rock mass boreability index (SRMBI), two different models including classification and regression tree (CART) analysis and multivariate regression analysis (MVRA) have been developed. As statistical indices, correlation coefficient (R ²), root mean square error (RMSE) and variance accounted for (VAF) were used to evaluate the efficiency of the developed models for determining the SRMBI of TBMs. According to the obtained results, it was observed that the performance of the CART model is better than the MVRA.

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Titel: TBM performance estimation using a classification and regression tree (CART) technique
verfasst von: Alireza Salimi
Roohollah Shirani Faradonbeh
Masoud Monjezi
Christian Moormann
Publikationsdatum: 18.11.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Bulletin of Engineering Geology and the Environment / Ausgabe 1/2018
Print ISSN: 1435-9529
Elektronische ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-016-0969-0

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