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https://hdl.handle.net/11499/6042
Title: | Prediction of hard rock TBM penetration rate using particle swarm optimization | Authors: | Yağız, Saffet Karahan, Halil |
Keywords: | Particle swarm optimization Rock mass properties TBM penetration rate Data sets Developed model Field machines Hard rocks Intact rocks Particle swarm optimization technique Penetration rates Predictive models Rock types Training data sets Tunnel boring machines Boring machines (machine tools) Earth boring machines Fracture mechanics Rock mechanics Rocks Particle swarm optimization (PSO) data set database optimization rock mass classification rock mechanics TBM tunneling |
Abstract: | The aim of this study is to predict the performance of tunnel boring machines (TBMS) using particle swarm optimization technique (PSO). With this aim, a database including intact rock parameters comprising of strength and brittleness, and rock mass properties such as distance between planes of weakness and orientation of discontinuities, together with field machine performance data, was established using data collected along a 7.5. km long hard rock mechanical tunnel. The particle swarm optimization technique was applied to develop new predictive model for TBM performance. Seven different PSO models were developed using the assortment of datasets having various percentages of rock type in the dataset. Additionally, the PSO model was developed using the entire dataset in random without paying attention to rock type to generalize the model. As a result of the developed models via a variety of generated testing and training datasets, it is concluded that Model 7 and its resultant equation are the most precise among the seven models tested. © 2011 Elsevier Ltd. | URI: | https://hdl.handle.net/11499/6042 https://doi.org/10.1016/j.ijrmms.2011.02.013 |
ISSN: | 1365-1609 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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