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• W2989404270 startingPage "107244" @default.
• W2989404270 abstract "The aim of present study is to propose new superior equations and introduce novel techniques for TBM performance prediction. To this end, correlations between the Rate of Penetration (ROP) and rock mass properties are investigated using four simple regression analyses. Based on these analyses, two non-linear multivariable equations are introduced and optimized by the Imperialist Competitive Algorithm (ICA). Then, two other distinct models are examined by using the Classification and Regression Tree (CART) and Genetic Expression Programming (GEP) techniques. The aforementioned methods are applied on a dataset from the Queens Tunnel, in New-York City with complex geology conditions. It was found that the models proposed by ICA, CART and GEP techniques have determination coefficient of 0.76, 0.82 and 0.72 for training data, and 0.62, 0.69 and 0.65 for testing data, respectively. The results showed the noticeable improvement of the predictions compare to previous studies." @default.
• W2989404270 created "2019-11-22" @default.
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• W2989404270 date "2020-02-01" @default.
• W2989404270 modified "2023-10-14" @default.
• W2989404270 title "Performance prediction of tunnel boring machine through developing high accuracy equations: A case study in adverse geological condition" @default.
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• W2989404270 doi "https://doi.org/10.1016/j.measurement.2019.107244" @default.
• W2989404270 hasPublicationYear "2020" @default.
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