Deformation Prediction and Inversion of Shuibuya Project Based on Artificial Neural Network and Genetic Algorithm

2012 ◽  
Vol 170-173 ◽  
pp. 2115-2118 ◽  
Author(s):  
Ling Cao ◽  
Zhen Biao Zhan ◽  
Yong Han
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Permanent Deformation ◽  
Model Parameters ◽  
Rock Fill ◽  
Artificial Neural ◽  
One Year ◽  
And Inversion ◽  
Concrete Face

he application of artificial neural network and genetic algorithm is made into the Shuibuya concrete face rock-fill dam project. At the beginning of design phase, genetic algorithm was used to predict the deformation of the dam; One year after the completion of construction, the rheological constitutive model parameters of Shuibuya concrete face rock-fill dam (CFRD) was inverted based on the monitoring data. And the permanent deformation of the dam was computed with the help of artificial neural network and genetic algorithm. The study result not only can accurately grasp the characteristic of Shuibuya CFRD, but also is propitious for the advancement of the computation theory about superhigh project.

scholarly journals Simulation and optimization of genetic algorithm-artificial neural network based air quality estimator

2020 ◽  
Vol 19 (2) ◽  
pp. 775
Author(s):  
Shirish Pandey ◽  
S. Hasan Saeed ◽  
N. R. Kidwai
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Air Quality ◽  
Estimation Model ◽  
Simulation And Optimization ◽  
Artificial Neural ◽  
Using Data ◽  
One Year ◽  
Intelligent Model

In this work intelligent model for estimation of the concentration of carbon monoxide in a polluted environment is developed on mat Lab platform. The results are validated using data collected from repository linked to University of California. The data records are over the duration of one year using E nose sensor placed in main city of Italy. The records are rectified and segmented at different length to extract the Base and Divergence Values features. An Artificial Neural Network Model (ANN) is developed and the result is validated manually. Another optimized Genetic Algorithm-Artificial Neural Network based air quality estimation model is developed which validate the result using artificial intelligence technique to get a better performance network.

scholarly journals Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammad Mehdi Arab ◽  
Abbas Yadollahi ◽  
Maliheh Eftekhari ◽  
Hamed Ahmadi ◽  
Mohammad Akbari ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Culture Medium ◽  
In Vitro Rooting ◽  
Artificial Neural

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

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