4.6 Article

Rock strength estimation: a PSO-based BP approach

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Prediction of the durability of limestone aggregates using computational techniques

Seyed Vahid Alavi Nezhad Khalil Abad et al.

NEURAL COMPUTING & APPLICATIONS (2018)

Article Computer Science, Artificial Intelligence

Developing a hybrid PSO-ANN model for estimating the ultimate bearing capacity of rock-socketed piles

Danial Jahed Armaghani et al.

NEURAL COMPUTING & APPLICATIONS (2017)

Article Computer Science, Artificial Intelligence

An optimized ANN model based on genetic algorithm for predicting ripping production

Edy Tonnizam Mohamad et al.

NEURAL COMPUTING & APPLICATIONS (2017)

Article Geosciences, Multidisciplinary

Prediction of the strength and elasticity modulus of granite through an expert artificial neural network

Danial Jahed Armaghani et al.

ARABIAN JOURNAL OF GEOSCIENCES (2016)

Article Computer Science, Interdisciplinary Applications

Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances

Danial Jahed Armaghani et al.

ENGINEERING WITH COMPUTERS (2016)

Article Computer Science, Interdisciplinary Applications

Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques

M. Monjezi et al.

ENGINEERING WITH COMPUTERS (2016)

Article Computer Science, Interdisciplinary Applications

Rock strength assessment based on regression tree technique

Maybelle Liang et al.

ENGINEERING WITH COMPUTERS (2016)

Article Environmental Sciences

Estimation of ground vibration produced by blasting operations through intelligent and empirical models

S. Ghoraba et al.

ENVIRONMENTAL EARTH SCIENCES (2016)

Article Engineering, Multidisciplinary

Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study

Hossein Rezaei et al.

JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE A (2016)

Article Engineering, Geological

Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique

Manoj Khandelwal et al.

GEOTECHNICAL AND GEOLOGICAL ENGINEERING (2016)

Article Geosciences, Multidisciplinary

Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles

Ehsan Momeni et al.

Earth Sciences Research Journal (2015)

Article Computer Science, Artificial Intelligence

An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining

Mohammad Rezaei et al.

NEURAL COMPUTING & APPLICATIONS (2014)

Article Environmental Sciences

Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks

Nurcihan Ceryan et al.

ENVIRONMENTAL EARTH SCIENCES (2013)

Article Computer Science, Artificial Intelligence

A neuro-fuzzy approach for prediction of longitudinal wave velocity

A. K. Verma et al.

NEURAL COMPUTING & APPLICATIONS (2013)

Article Geochemistry & Geophysics

Correlating P-wave Velocity with the Physico-Mechanical Properties of Different Rocks

Manoj Khandelwal

PURE AND APPLIED GEOPHYSICS (2013)

Article Computer Science, Interdisciplinary Applications

Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks

T. N. Singh et al.

ENGINEERING WITH COMPUTERS (2012)

Article Engineering, Geological

Use of the block punch test to predict the compressive and tensile strengths of rocks

D. A. Mishra et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2012)

Article Engineering, Environmental

Correlation between slake durability and rock properties for some carbonate rocks

Saffet Yagiz

BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2011)

Article Engineering, Geological

Artificial neural networks as a basis for new generation of rock failure criteria

Hosein Rafiai et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2011)

Article Automation & Control Systems

Signature verification (SV) toolbox: Application of PSO-NN

M. Taylan Das et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2009)

Article Energy & Fuels

Correlating static properties of coal measures rocks with P-wave velocity

Manoj Khandelwal et al.

INTERNATIONAL JOURNAL OF COAL GEOLOGY (2009)

Article Engineering, Geological

An Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters

I. Yilmaz et al.

ROCK MECHANICS AND ROCK ENGINEERING (2008)

Article Engineering, Geological

The effect of porosity on the relation between uniaxial compressive strength and point load index

S. Kahraman et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2005)

Article Automation & Control Systems

A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock

C Gokceoglu et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2004)

Article Engineering, Electrical & Electronic

Hybrid PSO-SQP for economic dispatch with valve-point effect

TAA Victoire et al.

ELECTRIC POWER SYSTEMS RESEARCH (2004)

Article Engineering, Geological

Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara Agglomerate

H Sonmez et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2004)

Article Engineering, Geological

Evaluation of the block punch index test with particular reference to the size effect, failure mechanism and its effectiveness in predicting rock strength

S Sulukcu et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2001)

Article Engineering, Geological

Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks

VK Singh et al.

INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES (2001)

Article Biochemical Research Methods

Artificial neural networks: fundamentals, computing, design, and application

IA Basheer et al.

JOURNAL OF MICROBIOLOGICAL METHODS (2000)