scholarly journals Modeling Self-Healing of Concrete Using Hybrid Genetic Algorithm–Artificial Neural Network

Materials ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 135 ◽  
Author(s):  
Ahmed Ramadan Suleiman ◽  
Moncef Nehdi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Self Healing ◽  
Artificial Neural

Prediction of soil wind erodibility using a hybrid Genetic algorithm – Artificial neural network method

CATENA ◽  
2020 ◽  
Vol 187 ◽  
pp. 104315 ◽  
Author(s):  
I. Kouchami-Sardoo ◽  
H. Shirani ◽  
I. Esfandiarpour-Boroujeni ◽  
A.A. Besalatpour ◽  
M.A. Hajabbasi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Efficiency Enhancement of Bidirectional Impulse Turbine Using Artificial Neural Network

2014 ◽  
Author(s):  
R. Badhurshah ◽  
A. Samad
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Stokes Equations ◽  
Hybrid Genetic Algorithm ◽  
Rotor Blades ◽  
Efficiency Enhancement ◽  
Impulse Turbine ◽  
Optimal Point ◽  
Artificial Neural

The work represents a systematic numerical optimization methodology using artificial neural network and hybrid genetic algorithm for a bi-directional axial impulse turbine used in wave energy harvesting system. Reynolds-averaged Navier-Stokes equations with k-ε turbulence model were discretized and solved for unstructured tetrahedral grid elements for flow analyses. Efficiency enhancement of the turbine was chosen as an objective. The design variables chosen were numbers of stator and rotor blades. The responses obtained from CFD analysis were used to train the neural network. The optimal point search from the network by hybrid genetic algorithm produced 13% increase in turbine efficiency. Detailed description of the methodology and analysis of the results has been presented in this paper.

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