Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning
Authors
Keywords
TBM performance prediction, Deep belief network (DBN), Yingsong Water Diversion Project, Field penetration index prediction
Journal
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
Volume -, Issue -, Pages 103636
Publisher
Elsevier BV
Online
2021-01-12
DOI
10.1016/j.tust.2020.103636
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep belief network based k-means cluster approach for short-term wind power forecasting
- (2018) Kejun Wang et al. ENERGY
- Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition
- (2017) Danial Jahed Armaghani et al. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
- Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs
- (2016) Alireza Salimi et al. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
- Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks
- (2015) Feng Liu et al. Entropy
- Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics
- (2015) A. Benato et al. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
- Deep learning
- (2015) Yann LeCun et al. NATURE
- A support vector regression model for predicting tunnel boring machine penetration rates
- (2014) Satar Mahdevari et al. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
- Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study
- (2014) O. Frough et al. ROCK MECHANICS AND ROCK ENGINEERING
- A new model for TBM performance prediction in blocky rock conditions
- (2014) A. Delisio et al. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
- Analysis and prediction of TBM performance in blocky rock conditions at the Lötschberg Base Tunnel
- (2012) A. Delisio et al. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
- Acoustic Modeling Using Deep Belief Networks
- (2011) Abdel-rahman Mohamed et al. IEEE Transactions on Audio Speech and Language Processing
- Deep, Big, Simple Neural Nets for Handwritten Digit Recognition
- (2010) Dan Claudiu Cireşan et al. NEURAL COMPUTATION
- Performance prediction of hard rock TBM using Rock Mass Rating (RMR) system
- (2010) Jafar Khademi Hamidi et al. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
- Application of two non-linear prediction tools to the estimation of tunnel boring machine performance
- (2009) S. Yagiz et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- TBM Performance Analysis in Pyroclastic Rocks: A Case History of Karaj Water Conveyance Tunnel
- (2009) J. Hassanpour et al. ROCK MECHANICS AND ROCK ENGINEERING
- Development of a rock mass characteristics model for TBM penetration rate prediction
- (2008) Q.M. Gong et al. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
- Utilizing rock mass properties for predicting TBM performance in hard rock condition
- (2007) Saffet Yagiz TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More