Al-Nahrain Journal for Engineering Sciences
Login
NJES
  • Home
  • Articles & Issues
    • Latest Issue
    • All Issues
  • Authors
    • Submit Manuscript
    • Guide for Authors
    • Submission Resources
    • Authorship
    • Article Processing Charges (APC)
  • Reviewers
    • Guide for Reviewers
    • Become a Reviewer
  • Policies
    • Publication Ethics
    • Plagiarism
    • Allegations of Misconduct
    • Appeals and Complaints
    • Corrections and Withdrawals
    • Open Access
    • Archiving Policy
    • Copyright Policy
  • About
    • About Journal
    • Aims and Scope
    • Editorial Team
    • Journal Insights
    • Peer Review Process
    • Abstracting and Indexing
    • Announcements
    • Contact

Search Results for N.J. Khalaf

Article
Seismic Evaluation and Retrofitting of an Existing Buildings-State of the Art

Haider Ali Abass, Husain Khalaf Jarallah

Pages: 52-75

PDF Full Text
Abstract

In this study, previous researches were reviewed in relation to the seismic evaluation and retrofitting of an existing building. In recent years, a considerable number of researches has been undertaken to determine the performance of buildings during the seismic events. Performance based seismic design is a modern approach to earthquake resistant design of reinforcement concrete buildings. Performance based design of building structures requires rigorous non-linear static analysis. In general, nonlinear static analysis or pushover analysis was conducted as an efficient instrument for performance-based design. Pushover analysis came into practice after 1970 year.  During the seismic event, a nonlinear static analysis or pushover analysis is used to analyze building under gravity loads and monotonically increasing lateral forces. These building were evaluated until a target displacement reached. Pushover analysis provides a better understanding of buildings seismic performance, also it traces the progression of damage and failure of structural components of buildings. 

Article
Enhancing Thermal Stability of Hybrid-Modified Local Asphalt

Riyam H. Khalaf, Mohammed A. Abed

Pages: 233-239

PDF Full Text
Abstract

Chemical additives and polymeric materials, selected for their compatibility and ability to improve asphalt's performance in demanding environments. Key additives, including Polyphosphoric Acid (PPA), Polyvinyl Acetate (PVAC) beads, Maleic Anhydride (MA), and Ethylene Vinyl Acetate (EVA) resin, were mixed in precise ratios with the asphalt binder. These additives were chosen to evaluate their effects on crucial performance indicators, such as the Penetration Index (PI) and activation energy, which measure the material’s thermal stability, flexibility, and resistance to deformation. Results demonstrated that the addition of these materials significantly increased the asphalt’s activation energy by up to 45.44%, enhancing its resistance to temperature fluctuations and providing better stability under various environmental stresses. The Penetration Index (PI) also improved notably, indicating that modified asphalt exhibits greater durability and reduced susceptibility to cracking or deformation under thermal changes. These enhancements contribute to lower road maintenance requirements and support greater energy efficiency in asphalt production and application processes. Compared to neat asphalt, the modified asphalt exhibited superior thermal stability, mechanical resilience, and overall performance, making it suitable for use in diverse climatic conditions. This study provides valuable insights into sustainable asphalt modification techniques, emphasizing the role of polymer and chemical additives in extending pavement lifespan and reducing environmental impact through improved material properties.

Article
Characterization and Fabrication of Ankle Foot Orthoses using Composite with Titanium Nanoparticles

N.J. Khalaf, Sabrine Ben Amor, Borhen Louhichi

Pages: 109-117

PDF Full Text
Abstract

Orthoses and prostheses were Chosen and laminated based on their high Yield, ultimate stresses, bending stresses, and fatigue limit. Response Surface Methodology (RSM) was utilized to find the best values for two parameters reinforcement perlon fiber and percent of Titanium Nanoparticle coupled with the matrix resin during optimization. The response surface methodology combined the expertise of mathematicians and statisticians to construct and analyze experimental models. Using this method, we identified 13 different lamination samples comprising a wide range of perlon number and Ti nano Wt% in their Perlon layer composition. All lamination materials defined by RSM methods and produced by a vacuum system were subjected to a battery of tests, with fatigue tests performed on the ideal laminating material in contrast to laminations created in the first study (Tensile test, Bending test, and Fatigue tests according to the ASTM D638 and D790 respectively). In comparison to the other 12 laminations tested using Design Expert version 10.0.2, the lamination with ten perlon layers and 0.75 percent Ti nano proved to be the strongest overall in terms of Yield, ultimate, and bending loads. This study used composite materials and titanium nanoparticles to characterize and fabricate ankle foot orthoses. Strength in bending should amount to about 70 MPa, around 85 MPa in tensile tension. Two empirical quadratic equations for the models of peak bending strength and maximum tensile stress with 95% confidence were created using the response surface approach and analysis of variance within the design of experiments software.

Article
Simplified Convolutional Neural Network Model for Automatic Classification of Retinal Diseases from Optical Coherence Tomography Images

Noor B. Khalaf, Hadeel K. Aljobouri, Mohammed S. Najim, Ilyas Çankaya

Pages: 314-319

PDF Full Text
Abstract

Optical coherence tomography (OCT) allows for direct and immediate imaging of the morphology of retinal tissue. It has become a crucial imaging modality for diagnosing eye problems in ophthalmology. One of the most significant morphological characteristics of the retina is the structure of the retinal layers, which provides important evidence for diagnostic purposes and is related to a variety of retinal diseases. In this paper, a convolutional neural network (CNN) model is proposed that can identify the difference between a normal retina and three common macular diseases: Diabetic macular edema (DME), Drusen, and Choroidal neovascularization (CNV). This proposed model was trained and tested on an open source dataset of OCT images also with professional disease classifications such as DME, CNV, Drusen, and Normal. The suggested model has achieved 98.3% overall classification accuracy, with only 7 wrong classifications out of 368 test samples. The suggested model significantly outperforms other models that made use of the identical dataset. The final results show that the suggested model is particularly adapted to the detection of retinal disorders in ophthalmology centers.

1 - 4 of 4 items

Search Parameters

×

The submission system is temporarily under maintenance. Please send your manuscripts to

Go to Editorial Manager
Journal Logo
Al-Nahrain Journal for Engineering Sciences (NJES)

College of Engineering, Al-Nahrain University

  • Copyright Policy
  • Terms & Conditions
  • Privacy Policy
  • Accessibility
  • Cookie Settings
Licensing & Open Access

CC BY NC 4.0 Logo Licensed under CC-BY-NC-4.0

This journal provides immediate open access to its content.

Editorial Manager Logo Elsevier Logo

Peer-review powered by Elsevier’s Editorial Manager®

Copyright © 2026 College of Engineering, Al-Nahrain University, its licensors, and contributors. All rights reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.