Application of deep neural networks in predicting the penetration rate of tunnel boring machines

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

(2018) Mohammadreza Koopialipoor et al.   ENGINEERING WITH COMPUTERS

A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls

(2018) Ebrahim Noroozi Ghaleini et al.   ENGINEERING WITH COMPUTERS

Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions

(2018) Mohammadreza Koopialipoor et al.   SOFT COMPUTING

Predicting tunnel boring machine performance through a new model based on the group method of data handling

(2018) Mohammadreza Koopialipoor et al.   Bulletin of Engineering Geology and the Environment

Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques

(2018) Behrouz Gordan et al.   ENGINEERING WITH COMPUTERS

Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

(2018) Mohammadreza Koopialipoor et al.   ENGINEERING WITH COMPUTERS

Performance prediction of tunnel boring machine through developing a gene expression programming equation

(2017) Danial Jahed Armaghani et al.   ENGINEERING WITH COMPUTERS

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

TBM tunnelling under adverse geological conditions: An overview

(2016) Qiuming Gong 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 Networks for Quantitative Analysis of a Gold Immunochromatographic Strip

(2016) Nianyin Zeng et al.   Cognitive Computation

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

An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite

(2014) Danial Jahed Armaghani et al.   Bulletin of Engineering Geology and the Environment

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

Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic

(2013) Ebrahim Ghasemi et al.   Bulletin of Engineering Geology and the Environment

Learning with Hierarchical-Deep Models

(2013) R. Salakhutdinov et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

(2012) Geoffrey Hinton et al.   IEEE SIGNAL PROCESSING MAGAZINE

Study of various models for estimation of penetration rate of hard rock TBMs

(2012) Ebrahim Farrokh et al.   TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY

Prediction of hard rock TBM penetration rate using particle swarm optimization

(2011) Saffet Yagiz et al.   INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES

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

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

Prediction of uniaxial compressive strength of sandstones using petrography-based models

(2007) K. Zorlu et al.   ENGINEERING GEOLOGY

Utilizing rock mass properties for predicting TBM performance in hard rock condition

(2007) Saffet Yagiz   TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY