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Prediction of Ultimate Soil Bearing Capacity for Shallow Strip Foundation on Sandy Soils by Using (ANN) Techniqu
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Bearing capacity of soil is an important factor in designing shallow foundations. It is directly related to foundation dimensions and consequently its performance. The calculations for obtaining the bearing capacity of a soil needs many varying parameters, for example soil type, depth of foundation, unit weight of soil, etc. which makes these calculation very variable–parameter dependent. This paper presents the results of comparison between the theoretical equation stated by Terzaghi and the Artificial Neural Networks (ANN) technique to estimate the ultimate bearing capacity of the strip shallow footing on sandy soils. The results show a very good agreement between the theoretical solution and the ANN technique. Results revealed that using ANN gave a very high correlation factor associated with the results obtained from Terzagih’s equation, besides little computation time needed compared with computation time needed when applying Terzagih’s equation.

Publication Date
Fri Jun 01 2007
Journal Name
Al-khwarizmi Engineering Journal
Correlation for fitting multicomponent vapor-liquid equilibria data and prediction of azeotropic behavior
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Correlation equations for expressing the boiling temperature as direct function of liquid composition have been tested successfully and applied for predicting azeotropic behavior of multicomponent mixtures and the kind of azeotrope (minimum, maximum and saddle type) using modified correlation of Gibbs-Konovalov theorem. Also, the binary and ternary azeotropic point have been detected experimentally using graphical determination on the basis of experimental binary and ternary vapor-liquid equilibrium data.

            In this study, isobaric vapor-liquid equilibrium for two ternary systems: “1-Propanol – Hexane – Benzene” and its binaries “1-Propanol –

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Publication Date
Fri Dec 30 2022
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review
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Lost circulation or losses in drilling fluid is one of the most important problems in the oil and gas industry, and it appeared at the beginning of this industry, which caused many problems during the drilling process, which may lead to closing the well and stopping the drilling process. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses

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Publication Date
Wed Sep 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
A Viscoplastic Modeling for Permanent Deformation Prediction of Rubberized and Conventional Mix Asphalt
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Publication Date
Sat Jun 02 2018
Journal Name
Agroforestry Systems
Biomass and buffer management practice effects on soil hydraulic properties compared to grain crops for claypan landscapes
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Publication Date
Wed Dec 01 2021
Journal Name
International Journal Bioautomation
Model for Prediction of the Weight and Height Measurements of Patients with Disabilities for Diagnosis and Therapy
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Background: Accurate measurement of a patient’s height and weight is an essential part of diagnosis and therapy, but there is some controversy as to how to calculate the height and weight of patients with disabilities. Objective: This study aims to use anthropometric measurements (arm span, length of leg, chest circumference, and waist circumference) to find a model (alternatives) that can allow the calculation of the height and the body weight of patients with disabilities. Additionally, a model for the prediction of weight and height measurements of patients with disabilities was established. Method: Four hander patients aged 20-80 years were enrolled in this study and divided into two groups, 210 (52.5%) male and 190 (47.5%) fe

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Publication Date
Sun Jul 03 2016
Journal Name
Journal Of Educational And Psychological Researches
Future Anxiety and Its Relation to Life Orientation among Male and Female Nurses Working in Gaza Strip Government Hospitals
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The study aimed to explore the relationship between future anxiety and life orientation of male and female nurses, working in government hospitals of Gaza Strip governorates. The study sample consisted of 228 nurses (131 male nurses and97 female nurses. To achieve the study objectives , the researcher used the future anxiety scale, prepared by the researcher, and life orientation scale prepared by Scheier and Craver (1985 ) and translated into Arabic by Bader Al-Ansari . The results indicated that the level of future anxiety among nurses working at government hospitals was (64.85%), a high percentage, whereas life orientation was (65.96%), a low percentage. Additionally , the results showed that the Pearson correlation coefficient betwee

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Publication Date
Mon Oct 04 2004
Journal Name
International Conference On Geotechnical Engineering
The Equivalent Grain Diameter for Soil Specific Surface Determination
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Publication Date
Mon Sep 11 2023
Journal Name
Applied Water Science
Hydrochemistry and water quality of shallow groundwater in the Tikrit area of Salah Al Din Province, Iraq
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Abstract<p>Salah Al-Din Provence is an active agriculture and population region. One of its primary water sources is groundwater, which suffers from a lack of information regarding water quality and hydrochemistry. In order to study those missing variables, 27 samples from wells of shallow tubes were collected for analyzing the relevant physicochemical indices that help to produce the Schoeller index, Piper diagram, and Gibbs plot. Piper diagram revealed a hydrochemistry behavior of different values along with the groundwater samples. The chemistry of wells was controlled primarily by the evaporation process according to the Gibbs plot. The values of the Schoeller index of the studied samples stated that 59% of</p> ... Show More
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Publication Date
Wed May 03 2023
Journal Name
Periodicals Of Engineering And Natural Sciences (pen)
Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks
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The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par

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Publication Date
Tue Oct 01 2019
Journal Name
2019 12th International Conference On Developments In Esystems Engineering (dese)
Roadway Deterioration Prediction Using Markov Chain Modeling (Wasit Governorate/ Iraq as a Case Study)
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