Prediction of CBR Value of Fine Grained Soils of Bengal Basin by Genetic Expression Programming, Artificial Neural Network and Krigging Method

2020 ◽  
Vol 95 (2) ◽  
pp. 190-196 ◽  
Author(s):  
Sk Kamrul Alam ◽  
Abhijit Mondal ◽  
Amit Shiuly
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bengal Basin ◽  
Genetic Expression ◽  
Fine Grained ◽  
Genetic Expression Programming ◽  
Artificial Neural

On the Application of Machine Learning in Savonius Wind Turbine Technology: An Estimation of Turbine Performance Using Artificial Neural Network and Genetic Expression Programming

2021 ◽  
pp. 1-47
Author(s):  
Umang H. Rathod ◽  
Vinayak Kulkarni ◽  
Ujjwal K. Saha
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Speed Ratio ◽  
Stream Velocity ◽  
Cross Sectional ◽  
Genetic Expression ◽  
Genetic Expression Programming ◽  
Artificial Neural ◽  
Input Variables

Abstract This paper addresses the application of artificial neural network (ANN) and genetic expression programming (GEP), the popular artificial intelligence and machine learning methods, in order to estimate the Savonius wind rotor's performance based on different independent design variables. Savonius wind rotor is one of the competent members of the vertical axis wind turbines (VAWTs) due to its advantageous qualities such as direction independency, design simplicity, ability to perform at low wind speeds, potent standalone system. The available experimental data on Savonius wind rotor have been used to train the ANN and GEP using MATLAB R2020b and GeneXProTools 5.0 software, respectively. The input variables used in ANN and GEP architecture include newly proposed design shape factors, number of blades and stages, gap and overlap lengths, height and diameter of the rotor, free stream velocity, end plate diameter and tip speed ratio, besides cross-sectional area of wind tunnel test section. Based on this, the unknown governing function constituted by the aforementioned input variables is established using ANN and GEP to approximate/forecast the rotor performance as an output. The governing equation formulated by ANN is in the form of weights and biases, while GEP provides it in the form of traditional mathematical functions. The trained ANN and GEP are capable to estimate the rotor performance with R2 ≈ 0.97 and R2 ≈ 0.65, respectively, in correlation with the reported experimental rotor performance.

Developing New Empirical Formulae for the Resilient Modulus of Fine-Grained Subgrade Soils Using a Large Long-Term Pavement Performance Dataset and Artificial Neural Network Approach

2021 ◽  
pp. 036119812110570
Author(s):  
Halil Ibrahim Fedakar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resilient Modulus ◽  
Constitutive Models ◽  
Pavement Performance ◽  
Dry Density ◽  
Fine Grained ◽  
Ann Models ◽  
Artificial Neural

Artificial neural network (ANN) has been successfully used for developing prediction models for resilient modulus (Mr). However, no reliable Mr formula derived from these models has been proposed in previous studies, although engineers/researchers need empirical formulae for hand calculation of Mr. Therefore, this study aimed to propose reliable empirical formulae for the Mr of fine-grained soils using ANN. For this purpose, thousands of ANN models were developed using the long-term pavement performance (LTPP) and external datasets. The input parameters were the percentage of soil particles passing through #200 sieve (P200), silt percentage (SP), clay percentage (CP), liquid limit (LL), plasticity index (PI), maximum dry density ([ρdry]max), optimum moisture content (wopt), confining pressure (σc), and nominal maximum axial stress (σz). The ANN models were compared with several constitutive models. The results indicate that the constitutive models failed to predict the Mr, and the best Mr predictions were obtained by the ANN-C9 (P200, SP, CP, LL, PI, σc, and σz), ANN-C10 (P200, SP, CP, [ρdry]max, wopt, σc, and σz), and ANN-C11 (P200, SP, CP, LL, PI, [ρdry]max, wopt, σc, and σz) models. Thus, the structures of these ANN models were formulated and proposed as the new empirical formulae for the Mr of fine-grained soils. Sensitivity analysis was also performed on these ANN models. It was determined that (ρdry)max is the most influential parameter in the ANN-C10 model, and LL is the most influential parameter in the ANN-C9 and ANN-C11 models. On the other hand, σc and σz are the least influential parameters.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural
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