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Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial voids ratio. Multi-layer perceptron training by the backpropagation algorithm was used in creating the network. It was found that both models can predict shear strength parameters for gypseous soils with good reliability. Sensitivity analysis of the first model indicated that dry unit weight and plasticity index have the most significant effect on the predicted cohesion. While in the second model, the results indicated that the gypsum content and plasticity index have the most significant effect on the predicted angle of internal friction.

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Publication Date
Fri Jul 01 2016
Journal Name
Journal Of Engineering
Determination of Mono-crystalline Silicon Photovoltaic Module Parameters Using Three Different Methods
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For modeling a photovoltaic module, it is necessary to calculate the basic parameters which control the current-voltage characteristic curves, that is not provided by the manufacturer. Generally, for mono crystalline silicon module, the shunt resistance is generally high, and it is neglected in this model. In this study, three methods are presented for four parameters model. Explicit simplified method based on an analytical solution, slope method based on manufacturer data, and iterative method based on a numerical resolution. The results obtained for these methods were compared with experimental measured data. The iterative method was more accurate than the other two methods but more complexity. The average deviation of

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Publication Date
Sat Jul 23 2022
Journal Name
Environmental Monitoring And Assessment
Impact of using magnetic water on the micro structure of leached saline-sodic soil
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Publication Date
Mon Jul 01 2019
Journal Name
Journal Of Physics: Conference Series
Determination of the Fertility of Southern Iraqi Soil Using Laser - Induced Breakdown Spectroscopy System
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In this work, seven soil samples were brought brought to study and analyses the element concentrations from different southern regions of Iraq using laser-induced breakdown spectroscopy (LIBS) technique. It has been documented as an atomic emission spectroscopy (AES) technique. Laser-induced plasma utilized to analyze elements in materials (gases, liquids, and solids). In order to analyze elements in materials (gases, liquids, and solid). The Nd: YAG laser excitation source at 1064 nm with pulse width 9 ns is used to generate power density of 5.5 x 1012 MW/mm2, with optical spectrum in the range 320-740 nm. From this investigation, the soil sample analysis of the southern cities of Iraqi, it is concluded that the rich soil element of P, Si,

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Publication Date
Thu Aug 20 2020
Journal Name
Geosciences
Thematic Maps for the Variation of Bearing Capacity of Soil Using SPTs and MATLAB
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The current study involves placing 135 boreholes drilled to a depth of 10 m below the existing ground level. Three standard penetration tests (SPT) are performed at depths of 1.5, 6, and 9.5 m for each borehole. To produce thematic maps with coordinates and depths for the bearing capacity variation of the soil, a numerical analysis was conducted using MATLAB software. Despite several-order interpolation polynomials being used to estimate the bearing capacity of soil, the first-order polynomial was the best among the other trials due to its simplicity and fast calculations. Additionally, the root mean squared error (RMSE) was almost the same for the all of the tried models. The results of the study can be summarized by the production

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Publication Date
Wed Nov 13 2019
Journal Name
International Journal Of Research In Pharmaceutical Sciences
Prediction of maternal diabetes and adverse neonatal outcome in normotensive pregnancy using serum uric acid
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Diabetes mellitus, with adverse neonatal events are challenging issues to all obstetricians and pediatricians, where uric acid could play a vital role. We aimed to assess the relationship and prognostic benefits of serum uric acid measured at about 20 weeks’ gestation in normotensive pregnancy, with subsequent maternal diabetes, and neonatal complications. All singleton normotensive pregnant women with normal blood glucose, serum creatinine, and weight before pregnancy, whom attended Medical City Hospital, Department of Obstetrics and Gynecology in Baghdad, were involved and regarded as the case group, on the condition that their serum uric acid measured at 20 weeks’ gestation > 3 mg/dl, but if ≤ 3 mg/dl, they would be regi

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Publication Date
Sun Jul 26 2026
Journal Name
Discover Artificial Intelligence
Multi-classification of autism spectrum disorder behavior for children using explainable artificial intelligence techniques
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Precise and interpretable classification of autism-related behaviors is important for initial diagnosis, personalized intervention, and support arrangements. This study proposes an interpretable machine learning (ML) model using Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to classify behavioral patterns into four categories (normal, mild, moderate, and severe) associated with Autism Spectrum Disorder (ASD) based on a custom 377-instance survey dataset from Iraqi parents and teachers of children aged 6-12. The model observes 16 key features across communication and social interaction, repetitive behaviors, language, and adaptive skills, preprocessed via interquartile range (IQR) outlier removal, me

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Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
Direct Shear Behavior of Fiber Reinforced Concrete Elements
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Improving the accuracy of load-deformation behavior, failure mode, and ultimate load capacity for reinforced concrete members subjected to in-plane loadings such as corbels, wall to foundation connections and panels need shear strength behavior to be included. Shear design in reinforced concrete structures depends on crack width, crack slippage and roughness of the surface of cracks.

This paper illustrates results of an experimental investigation conducted to investigate the direct shear strength of fiber normal strength concrete (NSC) and reactive powder concrete (RPC). The tests were performed along a pre-selected shear plane in concrete members named push-off specimens. The effectiveness of concrete compressiv

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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Permeability Prediction for Nahr-Umr Reservoir / Subba field by Using FZI Method
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 The permeability determination in the reservoirs that are anisotropic and heterogeneous is a complicated problem due to the limited number of wells that contain core samples and well test data. This paper presents hydraulic flow units and flow zone indicator for predicting permeability of rock mass from core for Nahr-Umr reservoir/ Subba field. The Permeability measurement is better found in the laboratory work on the cored rock that taken from the formation. Nahr-Umr Formation is the main lower cretaceous sandstone reservoir in southern of Iraq. This formation is made up mainly of sandstone. Nahr-Umr formation was deposited on a gradually rising basin floor. The digenesis of Nahr-Umr sediments is very important du

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Publication Date
Thu Jun 01 2017
Journal Name
Iosr Journal Of Computer Engineering
Lossy Image Compression Using Wavelet Transform, Polynomial Prediction And Block Truncation Coding
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Publication Date
Thu Nov 29 2018
Journal Name
Al-khwarizmi Engineering Journal
Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
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Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on

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