Prediction of hard rock TBM penetration rate using particle swarm optimization

Saffet Yagiz, Halil Karahan

Research output: Contribution to journalArticlepeer-review

106 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)427-433
Number of pages7
JournalInternational Journal of Rock Mechanics and Mining Sciences
Issue number3
Publication statusPublished - Apr 1 2011
Externally publishedYes


  • Particle swarm optimization
  • Rock mass properties
  • TBM penetration rate

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology

Fingerprint Dive into the research topics of 'Prediction of hard rock TBM penetration rate using particle swarm optimization'. Together they form a unique fingerprint.

Cite this