Ehsan Momeni, PhD
Ehsan Momeni, PhD
Assistant Professor of Geotechnical Engineering, Lorestan University
Email yang diverifikasi di live.utm.my - Beranda
Judul
Dikutip oleh
Dikutip oleh
Tahun
Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN
E Momeni, R Nazir, DJ Armaghani, H Maizir
Measurement 57, 122-131, 2014
1612014
Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks
E Momeni, DJ Armaghani, M Hajihassani, MFM Amin
Measurement 60, 50-63, 2015
1602015
Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach
ET Mohamad, DJ Armaghani, E Momeni, SVANK Abad
Bulletin of Engineering Geology and the Environment 74 (3), 745-757, 2015
1012015
An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite
DJ Armaghani, ET Mohamad, E Momeni, MS Narayanasamy
Bulletin of Engineering Geology and the Environment 74 (4), 1301-1319, 2015
802015
Correlation Between Unconfined Compressive Strength and Indirect Tensile Strength of Limestone Rock Samples
R Nazir, E Momeni, D JahedArmaghani
75*
Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting
DJ Armaghani, E Momeni, SVANK Abad, M Khandelwal
Environmental Earth Sciences 74 (4), 2845-2860, 2015
712015
Prediction of the strength and elasticity modulus of granite through an expert artificial neural network
DJ Armaghani, ET Mohamad, E Momeni, M Monjezi, MS Narayanasamy
Arabian Journal of Geosciences 9 (1), 48, 2016
672016
Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles
E Momeni, R Nazir, DJ Armaghani, H Maizir
Earth Sciences Research Journal 19 (1), 85-93, 2015
612015
Rock strength estimation: a PSO-based BP approach
ET Mohamad, DJ Armaghani, E Momeni, AH Yazdavar, M Ebrahimi
Neural Computing and Applications, 1-12, 0
51
Prediction of unconfined compressive strength of limestone rock samples using L-type Schmidt hammer
R Nazir, E Momeni, DJ Armaghani, MF Mohd Amin
Electr J Geotech Eng 18, 1767-1775, 2013
352013
Performance prediction of tunnel boring machine through developing a gene expression programming equation
DJ Armaghani, RS Faradonbeh, E Momeni, A Fahimifar, MM Tahir
Engineering with Computers 34 (1), 129-141, 2018
262018
Prediction of bearing capacity of thin-walled foundation: a simulation approach
E Momeni, DJ Armaghani, SA Fatemi, R Nazir
Engineering with Computers 34 (2), 319-327, 2018
192018
Experimental and intelligent techniques to estimate bearing capacity of cohesive soft soils reinforced with soil-cement columns
AR Bunawan, E Momeni, DJ Armaghani, ASA Rashid
Measurement 124, 529-538, 2018
172018
An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand
R Nazir, E Momeni, K Marsono, H Maizir
Jurnal Teknologi 72 (3), 2015
152015
Prediction of unconfined compressive strength of rocks: a review paper
E Momeni, R Nazir, DJ Armaghani, ET Mohamad
Jurnal Teknologi 77 (11), 43-50, 2015
142015
Comparative study on prediction of axial bearing capacity of driven piles in granular materials
E Momeni, H Maizir, N Gofar, R Nazir
Jurnal Teknologi 61 (3), 15-20, 2013
142013
Bearing capacity of precast thin-walled foundation in sand
E Momeni, R Nazir, DJ Armaghani, H Sohaie
Proceedings of the Institution of Civil Engineers-Geotechnical Engineering, 1-12, 2015
112015
Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study
H Rezaei, R Nazir, E Momeni
J Zhejiang Univ Sci A 17, 273-285, 2016
102016
Bearing Capacity of Shallow Foundation's Prediction through Hybrid Artificial Neural Networks
A Marto, M Hajihassani, E Momeni
Applied Mechanics and Materials 567, 681-686, 2014
92014
Uplift Resistance of Buried Pipelines Enhanced by Geogrid
K Faizi, DJ Armaghani, E Momeni, R Nazir, ET Mohamad
Soil Mechanics and Foundation Engineering 51 (4), 188-195, 2014
72014
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