Author

Momeni E

Lorestan University - Cited by 2,510 - Geotechnical Engineering - Rock Mechanics - Soil Mechanics - Reliability analysis - Applied artificial intelligence

Biography

Department of Earth Sciences, The University of Memphis, USA.  He  has published more than 30 articles, In the field of Environment, Geospatial Science, Natural Disasters,Earth Sciences, Geographic Information System. 
Title
Cited by
Year
Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN
E Momeni, R Nazir, DJ Armaghani, H MaizirMeasurement 57, 122-131, 2014201
318
2014
Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks
E Momeni, DJ Armaghani, M Hajihassani, MFM AminMeasurement 60, 50-63, 2015201
297
2015
Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach
ET Mohamad, D Jahed Armaghani, E Momeni, ...Bulletin of Engineering Geology and the Environment 74 (3), 745-757, 2015201
199
2015
An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite
D Jahed Armaghani, E Tonnizam Mohamad, E Momeni, ...Bulletin of engineering geology and the environment 74 (4), 1301-1319, 2015201
180
2015
Prediction of the strength and elasticity modulus of granite through an expert artificial neural network
DJ Armaghani, ET Mohamad, E Momeni, M Monjezi, MS NarayanasamyArabian Journal of Geosciences 9 (1), 48, 2016201
152
2016
Rock strength estimation: a PSO-based BP approach
ET Mohamad, DJ Armaghani, E Momeni, AH Yazdavar, M EbrahimiNeural Computing and Applications, 1-12, 2018201
148
2018
Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting
DJ Armaghani, E Momeni, SVANK Abad, M KhandelwalEnvironmental Earth Sciences 74 (4), 2845-2860, 2015201
141
2015
Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles
E Momeni, R Nazir, DJ Armaghani, H MaizirEarth Sciences Research Journal 19 (1), 85-93, 2015201
127
2015
Performance prediction of tunnel boring machine through developing a gene expression programming equation
D Jahed Armaghani, RS Faradonbeh, E Momeni, A Fahimifar, MM TahirEngineering with Computers 34 (1), 129-141, 2018201
91
2018
Gaussian process regression technique to estimate the pile bearing capacity
E Momeni, MB Dowlatshahi, F Omidinasab, H Maizir, DJ ArmaghaniArabian Journal for Science and Engineering 45 (10), 8255-8267, 2020202
64
2020
An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity
DJ Armaghani, H Harandizadeh, E Momeni, H Maizir, J ZhouArtificial Intelligence Review 55 (3), 2313-2350, 2022202
59
2022
An efficient optimal neural network based on gravitational search algorithm in predicting the deformation of geogrid-reinforced soil structures
E Momeni, A Yarivand, MB Dowlatshahi, DJ ArmaghaniTransportation Geotechnics 26, 100446, 2021202
59
2021
Prediction of unconfined compressive strength of limestone rock samples using L-type Schmidt hammer
R Nazir, E Momeni, DJ Armaghani, MF Mohd AminElectr J Geotech Eng 18, 1767-1775, 2013201
58
2013
Experimental and intelligent techniques to estimate bearing capacity of cohesive soft soils reinforced with soil-cement columns
AR Bunawan, E Momeni, DJ Armaghani, ASA RashidMeasurement 124, 529-538, 2018201
56
2018
Prediction of bearing capacity of thin-walled foundation: a simulation approach
E Momeni, DJ Armaghani, SA Fatemi, R NazirEngineering with Computers 34 (2), 319-327, 2018201
46
2018
Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study
H Rezaei, R Nazir, E MomeniJ Zhejiang Univ Sci A 17, 273-285, 2016201
42
2016
Application of group method of data handling technique in assessing deformation of rock mass
DJ Armaghani, E Momeni, PG Asteris1 1 (1), 001, 2020202
39
2020
Prediction of unconfined compressive strength of rocks: a review paper
E Momeni, R Nazir, DJ Armaghani, ET MohamadJurnal Teknologi 77 (11), 43-50, 2015201
36
2015
An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand
R Nazir, E Momeni, K Marsono, H MaizirJurnal Teknologi 72 (3), 2015201
31
2015
Comparative study on prediction of axial bearing capacity of driven piles in granular materials
E Momeni, H Maizir, N Gofar, R NazirJurnal Teknologi 61 (3), 15-20, 2013201
29
2013

Referred From: https://scholar.google.com/citations?hl=en&user=moH_4KsAAAAJ

Cited by
Citations 2510
h-index 23
i10-index 29
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