Analysis on prediction models of TBM performance: A review
Author(s) -
Hang-Lo Lee,
Ki-Il Song,
Gye-Chun Cho
Publication year - 2016
Publication title -
journal of korean tunnelling and underground space association
Language(s) - English
Resource type - Journals
eISSN - 2287-4747
pISSN - 2233-8292
DOI - 10.9711/ktaj.2016.18.2.245
Subject(s) - performance prediction , predictive modelling , computer science , selection (genetic algorithm) , process (computing) , machine learning , simulation , operating system
Prediction of TBM performance is very important for machine selection, and for reliable estimation of construction cost and period. The purpose of this research is to analyze the evaluation process of various prediction models for TBM performance and applied methodology. Based on the solid literature review since 2000, a classification system of TBM performance prediction model is proposed in this study. Classification system suggested in this study can be divided into two stages: selection of input parameter and application of prediction techniques. We also analyzed input and output parameters for prediction model and frequency of use. Lastly, the future research and development trend of TBM performance prediction is suggested.
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