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Prediction of bearing capacity of driven piles for Basrah governatore using SPT and MATLAB
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Based on the results of standard penetration tests (SPTs) conducted in Al-Basrah governorate, this research aims to present thematic maps and equations for estimating the bearing capacity of driven piles having several lengths. The work includes drilling 135 boreholes to a depth of 10 m below the existing ground level and three standard penetration tests (SPT) at depths of 1.5, 6, and 9.5 m were conducted in each borehole. MATLAB software and corrected SPT values were used to determine the bearing capacity of driven piles in Al-Basrah. Several-order interpolation polynomials are suggested to estimate the bearing capacity of driven piles, but the first-order polynomial is considered the most straightforward. Furthermore, the root means squared error (RMSE) for all suggested polynomials are roughly the same. The production of thematic maps demonstrates the variation in bearing capacity of driven piles over the entire territory of Al-Basrah governorate in correlation with different depths. The results of the statistical equations showed that there is good agreement with those obtained from the SPT data. When compared with the observed values from SPT, the allowable bearing capacity results for the driven piles ranged from (−3 to +38)%. The main results of this study showed a variation of 30% between calculated and estimated values of bearing capacity of driven piles for all lengths of piles at a 95% confidence interval.

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Publication Date
Fri Apr 01 2022
Journal Name
Civil Engineering Journal
Prediction of Urban Spatial Changes Pattern Using Markov Chain
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Urban land uses of all kinds are the constituent elements of the urban spatial structure. Because of the influence of economic and social factors, cities in general are characterized by the dynamic state of their elements over time. Urban functions occur in a certain way with different spatial patterns. Hence, urban planners and the relevant urban management teams should understand the future spatial pattern of these changes by resorting to quantitative models in spatial planning. This is to ensure that future predictions are made with a high level of accuracy so that appropriate strategies can be used to address the problems arising from such changes. The Markov chain method is one of the quantitative models used in spatial planning to ana

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Publication Date
Mon Mar 29 2021
Journal Name
Journal Of Engineering
Behavior of Defective Cast in Place Piles
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This paper deals with testing defected model piles in the soil in order to study their behavior. In this respect, the results of model pile tests are discussed either geotechnically or structurally according to the type of failure.   Two parameters were studied in order to evaluate the general behavior of defective piles. These parameters include the defect location and the defect type for floating and end bearing pile. The results of the experimental work indicated that the critical case for floating pile is seen to be when the defect of (5%) at the first third of the pile length at which the decrease in the bearing capacity is about (21%), while the decrease in the bearing capacity is found to be (14%) and (10%), when

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Publication Date
Mon Mar 29 2021
Journal Name
Journal Of Engineering
Behavior of Defective Cast in Place Piles
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This paper deals with testing defected model piles in the soil in order to study their behavior. In this respect, the results of model pile tests are discussed either geotechnically or structurally according to the type of failure.

 

Two parameters were studied in order to evaluate the general behavior of defective piles. These parameters include the defect location and the defect type for floating and end bearing pile. The results of the experimental work indicated that the critical case for floating pile is seen to be when the defect of (5%) at the first third of the pile length at which the decrease in the bearing capacity is about (21%), while the decrease in the bearing capacity is found to be (

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Geological Journal
Evaluating Machine Learning Techniques for Carbonate Formation Permeability Prediction Using Well Log Data
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Machine learning has a significant advantage for many difficulties in the oil and gas industry, especially when it comes to resolving complex challenges in reservoir characterization. Permeability is one of the most difficult petrophysical parameters to predict using conventional logging techniques. Clarifications of the work flow methodology are presented alongside comprehensive models in this study. The purpose of this study is to provide a more robust technique for predicting permeability; previous studies on the Bazirgan field have attempted to do so, but their estimates have been vague, and the methods they give are obsolete and do not make any concessions to the real or rigid in order to solve the permeability computation. To

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Publication Date
Fri Nov 29 2024
Journal Name
The Iraqi Geological Journal
Data Driven Approach for Predicting Pore Pressure of Oil and Gas Wells, Case Study of Iraq Southern Oilfields
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Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

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Publication Date
Sun Oct 19 2025
Journal Name
Lecture Notes In Networks And Systems
The Impact of Black Marketing on Purchasing Decisions: An Exploratory Study Using AI-Driven Analytical Tools
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Publication Date
Tue Nov 07 2023
Journal Name
Innovative Infrastructure Solutions
Enhancing load-bearing performance of hybrid recycled aggregate concrete-filled columns using SBR, steel fibers and polypropylene fibers
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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Artificial Neural Network (ANN) for Prediction of Viscosity Reduction of Heavy Crude Oil using Different Organic Solvents
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The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

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Publication Date
Wed Jun 20 2018
Journal Name
Bulletin Of Iraq Natural History Museum
ANNOTATED CHECKLIST OF REPTILIAN FAUNA OF BASRAH, SOUTH OF IRAQ
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Publication Date
Sun Jul 01 2018
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
ANNOTATED CHECKLIST OF REPTILIAN FAUNA OF BASRAH, SOUTH OF IRAQ
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    Basrah province is situated at the extreme south of Iraq,  it has an interesting reptile fauna (Squamata and Serpentes) and represents a land bridge between three different zoogeographical regions ( Oriental, Palaearctic and  Ethiopian).

     This situation gave Basrah province a topographic specific opportunity for raising its own faunal diversity including reptiles; in this study Basrah province was divided into four main zones: the cities and orchards, marshes and wetlands (sabkha), the true dessert, the seashore and Shat Al-Arab.

Forty nine reptile species were recorded including snakes, sea and fresh water turtles, and Lizards; brief notes and descriptions for the rare

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