Application of dimensional analysis and multi-gene genetic programming to predict the performance of tunnel boring machines
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- @Article{KAZEMI:2022:asoc,
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author = "Majid Kazemi and Reza Barati",
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title = "Application of dimensional analysis and multi-gene
genetic programming to predict the performance of
tunnel boring machines",
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journal = "Applied Soft Computing",
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volume = "124",
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pages = "108997",
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year = "2022",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2022.108997",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494622003222",
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keywords = "genetic algorithms, genetic programming, Tunnel boring
machines, Performance prediction, Dimensional analysis,
Multi-gene genetic programming, Practical models",
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abstract = "An accurate prediction of tunnel boring machine (TBM)
performance is one of the complex and crucial issues
encountered frequently in tunnel construction, which is
the aim of the present study. An improved methodology
using dimensional analysis (DA) and multi-gene genetic
programming (MGGP) is proposed to obtain a practical
and accurate model which can predict TBM performance.
Three dimensionless parameters are introduced by
applying DA to predict TBM performance more
efficiently. These parameters can represent TBM and
rock features. The MGGP, as a powerful technique for
developing a practical correlation model, was adopted
to develop highly accurate models using GPTIPS (Genetic
Programming Toolbox for the Identification of Physical
Systems). A well-known database of a hard rock
mechanized tunneling project of the Queens water
conveyance tunnel was used to evaluate the performance
of the proposed methodology. The performances of the
developed models were examined and compared with other
reported models using three statistical criteria.
Regarding the sum of squared deviations (SSD), the
developed model yielded 21.7percent better results than
the best existing model. Moreover, it was found that
the presented dimensionless parameters have physical
meaning and are much better parameters to develop a
model for TBM performance prediction",
- }
Genetic Programming entries for
Majid Kazemi
Reza Barati
Citations