Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods

Bridges are aging and deteriorating. Thus, the development of Bridge Management Systems (BMSs) became imperative nowadays. Condition assessment is one of the most critical and vital components of BMSs. Ground Penetrating Radar (GPR) is one of the non-destructive techniques (NDTs) that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. The objective of the proposed method is to develop standardized amplitude scale for bridge decks based on a hybrid optimization-decision making model. Shuffled frog leaping algorithm is employed to compute the optimum thresholds. Then, polynomial regression and artificial neural network models are designed to predict the prioritizing index based on a set of multi-criteria decision-making methods. The weibull distribution is utilized to capture the stochastic nature of deterioration of concrete bridge decks. Lastly, a case study is presented to demonstrate the capabilities of the proposed method.

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Artificial neural network models for forecasting and decision making

International Journal of Forecasting ◽

10.1016/0169-2070(94)90045-0 ◽

1994 ◽

Vol 10 (1) ◽

pp. 5-15 ◽

Cited By ~ 193

Author(s):

Tim Hill ◽

Leorey Marquez ◽

Marcus O'Connor ◽

William Remus

Keyword(s):

Neural Network ◽

Decision Making ◽

Artificial Neural Network ◽

Network Models ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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Applying artificial neural network models to clinical decision making.

Psychological Assessment ◽

10.1037/1040-3590.12.1.40 ◽

2000 ◽

Vol 12 (1) ◽

pp. 40-51 ◽

Cited By ~ 22

Author(s):

Rumi Kato Price ◽

Edward L. Spitznagel ◽

Thomas J. Downey ◽

Donald J. Meyer ◽

Nathan K. Risk ◽

...

Keyword(s):

Neural Network ◽

Decision Making ◽

Artificial Neural Network ◽

Clinical Decision Making ◽

Network Models ◽

Clinical Decision ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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Universal approximation with error bounds for dynamic artificial neural network models: A tutorial and some new results

2011 IEEE International Symposium on Computer-Aided Control System Design (CACSD) ◽

10.1109/cacsd.2011.6044542 ◽

2011 ◽

Cited By ~ 4

Author(s):

Kwang Ki Kevin Kim ◽

Ernesto Rios Patron ◽

Richard D. Braatz

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Error Bounds ◽

Network Models ◽

Universal Approximation ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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Bridging the Analytical and Artificial Neural Network Models for Keyhole Formation with Experimental Verification in Laser-melting Deposition: A Novel Approach

Results in Physics ◽

10.1016/j.rinp.2021.104440 ◽

2021 ◽

pp. 104440

Author(s):

Muhammad Arif Mahmood ◽

Andrei C. Popescu ◽

Mihai Oane ◽

Asma Channa ◽

Sabin Mihai ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Experimental Verification ◽

Network Models ◽

Laser Melting ◽

Neural Network Models ◽

Novel Approach ◽

Laser Melting Deposition ◽

Artificial Neural ◽

Artificial Neural Network Models

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Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽

10.3390/en14144242 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4242

Author(s):

Fausto Valencia ◽

Hugo Arcos ◽

Franklin Quilumba

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Finite Element ◽

Power Transformer ◽

Network Models ◽

Activation Function ◽

Neural Network Models ◽

Transformer Winding ◽

Artificial Neural ◽

Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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PSIII-5 Comparison of Bivariate Machine Learning and Linear Model for Genomic Prediction with Different Heritability, QTL and SNP Panel Scenarios

Journal of Animal Science ◽

10.1093/jas/skaa278.424 ◽

2020 ◽

Vol 98 (Supplement_4) ◽

pp. 230-231

Author(s):

Sunday O Peters ◽

Mahmut Sinecan ◽

Kadir Kizilkaya ◽

Milt Thomas

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Normal Distribution ◽

Genomic Prediction ◽

Network Models ◽

Bivariate Normal Distribution ◽

Snp Markers ◽

Neural Network Models ◽

Bivariate Normal ◽

Artificial Neural

Abstract This simulation study used actual SNP genotypes on the first chromosome of Brangus beef cattle to simulate 0.50 genetically correlated two traits with heritabilities of 0.25 and 0.50 determined either by 50, 100, 250 or 500 QTL and then aimed to compare the accuracies of genomic prediction from bivariate linear and artificial neural network with 1 to 10 neurons models based on G genomic relationship matrix. QTL effects of 50, 100, 250 and 500 SNPs from the 3361 SNPs of 719 animals were sampled from a bivariate normal distribution. In each QTL scenario, the breeding values (Σgijβj) of animal i for two traits were generated by using genotype (gij) of animal i at QTL j and the effects (βj) of QTL j from a bivariate normal distribution. Phenotypic values of animal i for traits were generated by adding residuals from a bivariate normal distribution to the breeding values of animal i. Genomic predictions for traits were carried out by bivariate Feed Forward MultiLayer Perceptron ANN-1–10 neurons and linear (GBLUP) models. Three sets of SNP panels were used for genomic prediction: only QTL genotypes (Panel1), all SNP markers, including the QTL (Panel2), and all SNP markers, excluding the QTL (Panel3). Correlations from 10-fold cross validation for traits were used to assess predictive ability of bivariate linear (GBLUP) and artificial neural network models based on 4 QTL scenarios with 3 Panels of SNP panels. Table 1 shows that the trait with high heritability (0.50) resulted in higher correlation than the trait with low heritability (0.25) in bivariate linear (GBLUP) and artificial neural network models. However, bivariate linear (GBLUP) model produced higher correlation than bivariate neural network. Panel1 performed the best correlations for all QTL scenarios, then Panel2 including QTL and SNP markers resulted in better prediction than Panel3.

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Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.3587 ◽

2011 ◽

Vol 403-408 ◽

pp. 3587-3593

Author(s):

T.V.K. Hanumantha Rao ◽

Saurabh Mishra ◽

Sudhir Kumar Singh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Network Models ◽

Neural Network Models ◽

Radial Basis ◽

Artificial Neural ◽

Artificial Neural Network Models ◽

Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

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