Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/6042
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yağız, Saffet | - |
dc.contributor.author | Karahan, Halil | - |
dc.date.accessioned | 2019-08-16T12:03:52Z | - |
dc.date.available | 2019-08-16T12:03:52Z | - |
dc.date.issued | 2011 | - |
dc.identifier.issn | 1365-1609 | - |
dc.identifier.uri | https://hdl.handle.net/11499/6042 | - |
dc.identifier.uri | https://doi.org/10.1016/j.ijrmms.2011.02.013 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | International Journal of Rock Mechanics and Mining Sciences | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Particle swarm optimization | en_US |
dc.subject | Rock mass properties | en_US |
dc.subject | TBM penetration rate | en_US |
dc.subject | Data sets | en_US |
dc.subject | Developed model | en_US |
dc.subject | Field machines | en_US |
dc.subject | Hard rocks | en_US |
dc.subject | Intact rocks | en_US |
dc.subject | Particle swarm optimization technique | en_US |
dc.subject | Penetration rates | en_US |
dc.subject | Predictive models | en_US |
dc.subject | Rock types | en_US |
dc.subject | Training data sets | en_US |
dc.subject | Tunnel boring machines | en_US |
dc.subject | Boring machines (machine tools) | en_US |
dc.subject | Earth boring machines | en_US |
dc.subject | Fracture mechanics | en_US |
dc.subject | Rock mechanics | en_US |
dc.subject | Rocks | en_US |
dc.subject | Particle swarm optimization (PSO) | en_US |
dc.subject | data set | en_US |
dc.subject | database | en_US |
dc.subject | optimization | en_US |
dc.subject | rock mass classification | en_US |
dc.subject | rock mechanics | en_US |
dc.subject | TBM | en_US |
dc.subject | tunneling | en_US |
dc.title | Prediction of hard rock TBM penetration rate using particle swarm optimization | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 48 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.startpage | 427 | - |
dc.identifier.startpage | 427 | en_US |
dc.identifier.endpage | 433 | en_US |
dc.authorid | 0000-0001-5346-5686 | - |
dc.identifier.doi | 10.1016/j.ijrmms.2011.02.013 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopus | 2-s2.0-79952623576 | en_US |
dc.identifier.wos | WOS:000288425100009 | en_US |
dc.identifier.scopusquality | Q1 | - |
dc.owner | Pamukkale University | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | 10.08. Geological Engineering | - |
crisitem.author.dept | 10.02. Civil Engineering | - |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
CORE Recommender
SCOPUSTM
Citations
188
checked on Aug 17, 2024
WEB OF SCIENCETM
Citations
150
checked on Aug 9, 2024
Page view(s)
52
checked on Aug 24, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.