Application of dimensional analysis and multi-gene genetic programming to predict the performance of tunnel boring machines

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{KAZEMI:2022:asoc,

• author = "Majid Kazemi and Reza Barati",
• title = "Application of dimensional analysis and multi-gene genetic programming to predict the performance of tunnel boring machines",
• journal = "Applied Soft Computing",
• volume = "124",
• pages = "108997",
• year = "2022",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2022.108997",
• URL = "https://www.sciencedirect.com/science/article/pii/S1568494622003222",
• keywords = "genetic algorithms, genetic programming, Tunnel boring machines, Performance prediction, Dimensional analysis, Multi-gene genetic programming, Practical models",
• 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