Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postponement of delay of interim payments is at the forefront of delay factors caused by the employer’s decision. Even the least one is to leave the job site caused by the contractor’s second part of the contract, the repeated unjustified stopping of the work at the site, without permission or notice from the client’s representatives. The developed model was applied to about 97 projects and used as a prediction model. The decision tree model shows higher accuracy in the prediction.
Storage tanks condition and integrity is maintained by joint application of coating and cathodic protection. Iraq southern region rich in oil and petroleum product refineries need and use plenty of aboveground storage tanks. Iraq went through conflicts over the past thirty five years resulting in holding the oil industry infrastructure behind regarding maintenance and modernization. The primary concern in this work is the design and implementation of cathodic protection systems for the aboveground storage tanks farm in the oil industry.
Storage tank external base area and tank internal surface area are to be protected against corrosion using impressed current and sacrificial anode cathodic protection systems. Int
... Show MoreMaximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a ty
... Show MoreIn the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and
... Show MoreIn the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and
... Show MoreIn the last few years, the use of artificial neural network analysis has increased, particularly, in geotechnical engineering problems and has demonstrated some success. In this research, artificial neural network analysis endeavors to predict the relationship between physical and mechanical properties of Baghdad soil by making different trials between standard penetration test, liquid limit, plastic limit, plasticity index, cohesion, angle of internal friction, and bearing capacity. The analysis revealed that the changes in natural water content and plastic limit have a great effect on the cohesion of soil and the angle of internal friction, respectively. . On the other hand, the liquid limit has a great impact on the bearing capacity and
... Show MoreThe most common cause of upper respiratory tract infection is coronavirus, which has a crown appearance due to the existence of spikes on its envelope. D-dimer levels in the plasma have been considered a prognostic factor for COVID-19 patients.
The aim of the study is to demonstrate the role of COVID-19 on coagulation parameters D-dimer and ferritin with their association with COVID-19 severity and disease progression in a single-center study.
Maulticollinearity is a problem that always occurs when two or more predictor variables are correlated with each other. consist of the breach of one basic assumptions of the ordinary least squares method with biased estimates results, There are several methods which are proposed to handle this problem including the method To address a problem and method To address a problem , In this research a comparisons are employed between the biased method and unbiased method with Bayesian using Gamma distribution method addition to Ordinary Least Square metho
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