Related references
Note: Only part of the references are listed.Three hybrid intelligent models in estimating flyrock distance resulting from blasting
Mohammadreza Koopialipoor et al.
ENGINEERING WITH COMPUTERS (2019)
A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls
Ebrahim Noroozi Ghaleini et al.
ENGINEERING WITH COMPUTERS (2019)
Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions
Mohammadreza Koopialipoor et al.
SOFT COMPUTING (2019)
Predicting tunnel boring machine performance through a new model based on the group method of data handling
Mohammadreza Koopialipoor et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2019)
Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques
Behrouz Gordan et al.
ENGINEERING WITH COMPUTERS (2019)
Overbreak prediction and optimization in tunnel using neural network and bee colony techniques
Mohammadreza Koopialipoor et al.
ENGINEERING WITH COMPUTERS (2019)
A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels
Mohammadreza Koopialipoor et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2019)
Performance prediction of tunnel boring machine through developing a gene expression programming equation
Danial Jahed Armaghani et al.
ENGINEERING WITH COMPUTERS (2018)
A Risk-Based Technique to Analyze Flyrock Results Through Rock Engineering System
Mahdi Hasanipanah et al.
GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2018)
Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition
Danial Jahed Armaghani et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2017)
TBM tunnelling under adverse geological conditions: An overview
Qiuming Gong et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2016)
Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs
Alireza Salimi et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2016)
Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip
Nianyin Zeng et al.
COGNITIVE COMPUTATION (2016)
Prediction of penetration per revolution in TBM tunneling as a function of intact rock and rock mass characteristics
A. Benato et al.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2015)
An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: a study on Main Range granite
Danial Jahed Armaghani et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2015)
Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic
Ebrahim Ghasemi et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2014)
A support vector regression model for predicting tunnel boring machine penetration rates
Satar Mahdevari et al.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2014)
Learning with Hierarchical-Deep Models
Ruslan Salakhutdinov et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey Hinton et al.
IEEE SIGNAL PROCESSING MAGAZINE (2012)
Study of various models for estimation of penetration rate of hard rock TBMs
Ebrahim Farrokh et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2012)
Prediction of hard rock TBM penetration rate using particle swarm optimization
Saffet Yagiz et al.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2011)
Application of two non-linear prediction tools to the estimation of tunnel boring machine performance
S. Yagiz et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2009)
Development of a rock mass characteristics model for TBM penetration rate prediction
Q. M. Gong et al.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2009)
Prediction of uniaxial compressive strength of sandstones using petrography-based models
K. Zorlu et al.
ENGINEERING GEOLOGY (2008)
Utilizing rock mass properties for predicting TBM performance in hard rock condition
Saffet Yagiz
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2008)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
A fast learning algorithm for deep belief nets
Geoffrey E. Hinton et al.
NEURAL COMPUTATION (2006)
An improved backpropagation algorithm to avoid the local minima problem
XG Wang et al.
NEUROCOMPUTING (2004)
Modelling TBM performance with artificial neural networks
AG Benardos et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2004)
TBM performance estimation using rock mass classifications
M Sapigni et al.
INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2002)
Share Paper
