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Mitigating Reflection Cracking in Asphalt Concrete Overlays with ECC and Geotextile
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The rehabilitation of deteriorated pavements using Asphalt Concrete (AC) overlays consistently confronts the reflection cracking challenge, where inherent cracks and joints from an existing pavement layer are mirrored in the new overlay. To address this issue, the current study evaluates the effectiveness of Engineered Cementitious Composite (ECC) and geotextile fabric as mitigation strategies. ECC, characterized by its tensile ductility, fracture resistance, and high deformation capacity, was examined in interlayer thicknesses of 7, 12, and 17 mm. Additionally, the impact of geotextile fabric positioning at the base and at 1/3 depth of the AC specimen was explored. Utilizing the Overlay Testing Machine (OTM) for evaluations, the research demonstrated that ECC17 significantly mitigated reflection cracking, showing a notable 764% increase in the number of load cycles to failure (Nf) compared to the Geotextile Base (GB) specimen. Against the Reference Specimen (RS), ECC17 exhibited a remarkable 1307% enhancement in Nf values, underscoring its effectiveness. Geotextile fabric, particularly at 1/3 depth, demonstrated notable resistance but was overshadowed by the performance of ECC interlayers. The results clearly indicate that ECC, especially ECC17, stands out as an effective solution for mitigating reflection cracking, including joints, in AC overlays.

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
Removing of Methylene Blue Dye from its Aqueous Solutions Using Polyacrylonitrile/Iron Oxide/Graphene Oxide
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      Organic contaminants are used to be found in industrial wastewater treatment procedures, and heavy metal ion removal is difficult. Photo Fenton reaction activity was exploited in this study to decompose organic contaminants using a functional composite hydrogel. Polyacrylonitrile (PAN), Fe3O4 particles, and graphene oxide make up the hydrogel (GO). It is made from GO/ Fe3O4 and is made using the precipitation technique. GO is made from graphite using the Hummers process. And it has exceptional mechanical strength and Photo-Fenton activity as a result of various breakdown data that were influenced differently, such as H2O2 concentration, dye concentration, temper

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Publication Date
Fri Dec 01 2023
Journal Name
Baghdad Science Journal
Effect of Chromium (VI) on the Oxidation of Methylene Blue Dye by Fe3O4/ Chitosan Composite
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     Heavy metal ion removal from industrial wastewater treatment systems is still difficult because it contains organic contaminants. In this study, functional composite hydrogels with photo Fenton reaction activity were used to decompose organic contaminants. Fe3O4 Nanoparticle, chitosan (CS), and other materials make up the hydrogel. There are different factors that affected Photo-Fenton activity including (pH, H2O2 conc., temp., and exposure period). Atomic force microscopy was used to examine the morphology of the composite and its average diameter (AFM). After 60 minutes of exposure to UV radiation, CS/ Fe3O4 hydrogel composite had degraded methylene blue (M.B.)

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Publication Date
Thu Aug 31 2017
Journal Name
Journal Of Engineering
Finite Element Analysis of UHPC Corbels
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   Finite element method is the most widely numerical technique used in engineering field. Through the study of behavior of concrete material properties, various concrete constitutive laws  and failure criteria have been developed to model the behavior of concrete. A feature of the Finite Element program (ATENA) is used in this study to model the behavior of UHPC corbel under concentrated load only. The Finite Element (FE) model is followed by verification against experimental results. Some variable effects on the shear capacity of the UHPC corbels are also demonstrated in a parametric study. A proposed design equation of shear strength of UHPC corbel was presented and checked with numerical results.
 

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Publication Date
Thu Nov 30 2023
Journal Name
Iraqi Journal Of Science
An Artificial Intelligence-based Proactive Network Forensic Framework
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     is at an all-time high in the modern period, and the majority of the population uses the Internet for all types of communication. It is great to be able to improvise like this. As a result of this trend, hackers have become increasingly focused on attacking the system/network in numerous ways. When a hacker commits a digital crime, it is examined in a reactive manner, which aids in the identification of the perpetrators. However, in the modern period, it is not expected to wait for an attack to occur. The user anticipates being able to predict a cyberattack before it causes damage to the system. This can be accomplished with the assistance of the proactive forensic framework presented in this study. The proposed system combines

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Publication Date
Wed Aug 30 2023
Journal Name
Iraqi Journal Of Science
Network Traffic Prediction Based on Time Series Modeling
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    Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and

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Publication Date
Tue Sep 25 2018
Journal Name
Iraqi Journal Of Science
Age Estimation Using Support Vector Machine
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Recently there has been an urgent need to identify the ages from their personal pictures and to be used in the field of security of personal and biometric, interaction between human and computer, security of information, law enforcement. However, in spite of advances in age estimation, it stills a difficult problem. This is because the face old age process is determined not only by radical factors, e.g. genetic factors, but also by external factors, e.g. lifestyle, expression, and environment. This paper utilized machine learning technique to intelligent age estimation from facial images using support vector machine (SVM) on FG_NET dataset. The proposed work consists of three phases: the first phase is image preprocessing include four st

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Publication Date
Thu Feb 28 2019
Journal Name
Iraqi Journal Of Science
Skin Detection using Improved ID3 Algorithm
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Skin detection is classification the pixels of the image into two types of pixels skin and non-skin. Whereas, skin color affected by many issues like various races of people, various ages of people gender type. Some previous researchers attempted to solve these issues by applying a threshold that depends on certain ranges of skin colors. Despite, it is fast and simple implementation, it does not give a high detection for distinguishing all colors of the skin of people. In this paper suggests improved ID3 (Iterative Dichotomiser) to enhance the performance of skin detection. Three color spaces have been used a dataset of RGB obtained from machine learning repository, the University of California Irvine (UCI), RGB color space, HSV color sp

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Publication Date
Tue Sep 29 2020
Journal Name
Iraqi Journal Of Science
Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses
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Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.

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Publication Date
Sun Mar 26 2023
Journal Name
Wasit Journal Of Pure Sciences
Covid-19 Prediction using Machine Learning Methods: An Article Review
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The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

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Publication Date
Thu Aug 01 2024
Journal Name
Advances In Science And Technology Research Journal
Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
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