Preferred Language
Articles
/
joe-1486
Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks
...Show More Authors

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944.

 

Crossref
View Publication Preview PDF
Quick Preview PDF
Publication Date
Thu Jan 03 2019
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and Artificial Neural Network
...Show More Authors

Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

... Show More
Publication Date
Thu Aug 31 2017
Journal Name
Journal Of Engineering
Optimum Dimensions of Hydraulic Structures and Foundation Using Genetic Algorithm coupled with Artificial Neural Network
...Show More Authors

      A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure aga

... Show More
View Publication Preview PDF
Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology
Water quality assessment and sodium adsorption ratio prediction of Tigris River using artificial neural network
...Show More Authors

Publication Date
Thu Oct 01 2020
Journal Name
Journal Of Engineering Science And Technology (jestec)
Water Quality Assessment and Sodium Adsorption Ratio Prediction of Tigris River Using Artificial Neural Network
...Show More Authors

Sodium adsorption ratio (SAR) is considered as a measure of the water suitability for irrigation usage. This study examines the effect of the physicochemical parameters on water quality and SAR, which included Calcium(Ca+2), Magnesium(Mg+2), Sodium (Na+), Potassium (K), Chloride (Cl-), Sulfate(SO4-2), Carbonate (CO3-2), Bicarbonate (HCO3-), Nitrate (NO3-), Total Hardness (TH), Total Dissolved Salts (TDS), Electrical Conductivity (EC), degree of reaction (DR), Boron (B) and the monthly and annually flow discharge (Q). The water samples were collected from three stations across the Tigris River in Iraq, which flows through Samarra city (upstream), Baghdad city (central) and the end of Kut city (downstream) for the periods of 2016-201

... Show More
Publication Date
Sat Jul 01 2023
Journal Name
Journal Of Engineering
Effect of Heat Treatments and Carbon Content on the Damping Properties of Structural Steel
...Show More Authors

Low- and medium-carbon structural steel components face random vibration and dynamic loads (like earthquakes) in many applications. Thus a modification to improve their mechanical properties, essentially damping properties, is required. The present study focuses on improving and developing these properties, significantly dampening properties, without losing the other mechanical properties. The specimens used in the present study are structural steel ribbed bar ISO 6935 subjected to heating temperatures of (850, 950, and 1050) ˚C, and cooling schemes of annealing, normalizing, sand, and quenching was selected. The damping properties of the specimens were measured experimentally with the area under the curve for the loadi

... Show More
View Publication Preview PDF
Crossref
Publication Date
Wed Mar 01 2023
Journal Name
Iraqi Journal Of Physics
Theoretical Computation of Electron Density in Laser-Induced Carbon Plasma using Anisimov Model
...Show More Authors

In this work, electron number density calculated using Matlab program code with the writing algorithm of the program. Electron density was calculated using Anisimov model in a vacuum environment. The effect of spatial coordinates on the electron density was investigated in this study. It was found that the Z axis distance direction affects the electron number density (ne). There are many processes such as excitation; ionization and recombination within the plasma that possible affect the density of electrons. The results show that as Z axis distance increases electron number density decreases because of the recombination of electrons and ions at large distances from the target and the loss of thermal energy of the electrons in high distance

... Show More
Publication Date
Fri Jul 01 2022
Journal Name
Iop Conference Series: Earth And Environmental Science
Computer Model Application for Sorting and Grading Citrus Aurantium Using Image Processing and Artificial Neural Network
...Show More Authors
Abstract<p>This study was conducted in College of Science \ Computer Science Department \ University of Baghdad to compare between automatic sorting and manual sorting, which is more efficient and accurate, as well as the use of artificial intelligence in automated sorting, which included artificial neural network, image processing, study of external characteristics, defects and impurities and physical characteristics; grading and sorting speed, and fruits weigh. the results shown value of impurities and defects. the highest value of the regression is 0.40 and the error-approximation algorithm has recorded the value 06-1 and weight fruits fruit recorded the highest value and was 138.20 g, Gradin</p> ... Show More
View Publication
Scopus (3)
Crossref (1)
Scopus Crossref
Publication Date
Fri Jun 01 2007
Journal Name
Journal Of Al-nahrain University Science
ON THE GREEDY RADIAL BASIS FUNCTION NEURAL NETWORKS FOR APPROXIMATION MULTIDIMENSIONAL FUNCTIONS
...Show More Authors

The aim of this paper is to approximate multidimensional functions by using the type of Feedforward neural networks (FFNNs) which is called Greedy radial basis function neural networks (GRBFNNs). Also, we introduce a modification to the greedy algorithm which is used to train the greedy radial basis function neural networks. An error bound are introduced in Sobolev space. Finally, a comparison was made between the three algorithms (modified greedy algorithm, Backpropagation algorithm and the result is published in [16]).

View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Tue Jun 22 2021
Journal Name
Expert Systems
Hybrid intelligent technology for plant health using the fusion of evolutionary optimization and deep neural networks
...Show More Authors

Scopus (2)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Sun Dec 17 2017
Journal Name
Al-khwarizmi Engineering Journal
Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
...Show More Authors

In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as  a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid

... Show More
View Publication Preview PDF