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Optimized stress-strain modeling of eco-friendly fiber-reinforced concrete members using meta-heuristic algorithms
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Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partially replaced by ground granulated blast furnace slag (GGBFS) with various amounts to make the concrete eco-friendly. The concrete was reinforced with several quantities of PP fiber. Specific cases of beams and cylinders made from PFRC were examined to learn more about their performance. The research contributes valuable insights to eco-friendly concrete design by integrating industrial byproducts (GGBFS) and non-metallic fibers, aligning with sustainable construction trends. The study demonstrates that adding sustainable fibers to concrete improves its structural integrity while lessening its environmental impact. Experimental testing validates the proposed model, showing a significant connection between the expected and actual stress-strain behavior. In terms of absolute relative error (ARE), the dataset proves that the suggested model has both the greatest (ARE 5 %) and worst (ARE > 15 %) frequencies. The proposed model demonstrates promising accuracy (R-value = 0.9975) and highlights the effectiveness of PSO in parameter optimization. Additionally, the usage of GGBFS instead of OPC resulted in CO2 reduction up to 42 %. Comparative analysis of the proposed model against existing models registered an excellent forecasted accuracy.

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Publication Date
Mon May 19 2025
Journal Name
Plos One
Inverse modeling, analysis and control of twin rotor aerodynamic systems with optimized artificial intelligent controllers
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This paper suggests a novel optimal inverse Radial Basis Function (RBF) neural network model for the control of Twin Rotor Aerodynamic Systems (TRAS), such as Multi-Input–Multi-Output (MIMO) systems with high nonlinearity and coupling effects between channels. After analyzing and linearizing the dynamic model, TRAS is decoupled into two Single Input Single Output (SISO) systems, thereby creating vertical (pitch model) and horizontal (yaw model) systems. The relationship between the output angle of each subsystem and the input voltage is modeled using the inverse RBF neural network. The weights, biases, centers and widths of the Gaussian function are unknown parameters of the proposed inverse neural model, and they are obtained usi

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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Engineering
Strengthening and Closing Cracks for Existing Reinforced Concrete Girders Using Externally Post-Tensioned Tendons
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This research is devoted to study the strengthening technique for the existing reinforced concrete beams using external post-tensioning. An analytical methodology is proposed to predict the value of the effective prestress force for the external tendons required to close cracks in existing beams. The external prestressing force required to close cracks in existing members is only a part from the total strengthening force.
A computer program created by Oukaili (1997) and developed by Alhawwassi (2008) to evaluate curvature and deflection for reinforced concrete beams or internally prestressed concrete beams is modified to evaluate the deflection and the stress of the external tendons for the externally strengthened beams using Matlab

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Publication Date
Wed Jul 01 2026
Journal Name
Next Materials
Eco-friendly fabrication of plasma-activated jute-cotton blended fabric coated with green-synthesized AgNPs for antimicrobial applications
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Publication Date
Sun Mar 01 2026
Journal Name
Journal Of Genetic Engineering And Biotechnology
Broccoli-mediated gold nanoparticles: Eco-friendly synthesis and nano-bio interactions promoting wound healing and targeted cytotoxicity
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Biogenic gold nanoparticles (AuNPs) were synthesized using broccoli extract to assess their antioxidant activity, wound-healing potential, and selective anticancer effects. Green synthesis with broccoli offers an environmentally friendly way to produce stable and biocompatible nanomaterials. In this study, Brassica oleracea aqueous extract served as both the reducing and capping agent, producing AuNPs with a characteristic surface plasmon resonance peak at 560 nm and a well-defined cubic crystalline structure confirmed by XRD. TEM analysis showed uniformly dispersed, semi-spherical nanoparticles with an average size of 7.5 ± 3.6 nm. The biosynthesized AuNPs exhibited potent antioxidant activity, achieving 91.2 % DPPH scavenging at 100 µg/

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Publication Date
Sun Jun 06 2021
Journal Name
Materials
Strengthening of Continuous Reinforced Concrete Deep Beams with Large Openings Using CFRP Strips
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To accommodate utilities in buildings, different sizes of openings are provided in the web of reinforced concrete deep beams, which cause reductions in the beam strength and stiffness. This paper aims to investigate experimentally and numerically the effectiveness of using carbon fiber reinforced polymer (CFRP) strips, as a strengthening technique, to externally strengthen reinforced concrete continuous deep beams (RCCDBs) with large openings. The experimental work included testing three RCCDBs under five-point bending. A reference specimen was prepared without openings to explore the reductions in strength and stiffness after providing large openings. Openings were created symmetrically at the center of spans of the other specimens

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Publication Date
Fri Sep 26 2025
Journal Name
Applied Data Science And Analysis
Deep Learning in Genomic Sequencing: Advanced Algorithms for HIV/AIDS Strain Prediction and Drug Resistance Analysis
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Genome sequencing has significantly improved the understanding of HIV and AIDS through accurate data on viral transmission, evolution and anti-therapeutic processes. Deep learning algorithms, like the Fined-Tuned Gradient Descent Fused Multi-Kernal Convolutional Neural Network (FGD-MCNN), can predict strain behaviour and evaluate complex patterns. Using genotypic-phenotypic data obtained from the Stanford University HIV Drug Resistance Database, the FGD-MCNN created three files covering various antiretroviral medications for HIV predictions and drug resistance. These files include PIs, NRTIs and NNRTIs. FGD-MCNNs classify genetic sequences as vulnerable or resistant to antiretroviral drugs by analyzing chromosomal information and id

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Publication Date
Wed Feb 29 2012
Journal Name
Al-khwarizmi Engineering Journal
Effect of Construction Joints on Performance of Reinforced Concrete Beams
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Construction joints are stopping places in the process of placing concrete, and they are required because in many structures it is impractical to place concrete in one continuous operation. The amount of concrete that can be placed at one time is governed by the batching and mixing capacity and by the strength of the formwork. A good construction joint should provide adequate flexural and shear continuity through the interface.

In this study, the effect of location of construction joints on the performance of reinforced concrete structural elements is experimentally investigated.

Nineteen beam specimens with dimensions of 200×200×950 mm were tested. The variables investigated are the location of the construction joints

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Publication Date
Thu Nov 18 2021
Journal Name
Eureka: Physics And Engineering
Experimental implementation of Raman scattering spectroscopy, photoluminescence and some optical properties of silver nanoparticles created by eco-friendly technique
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The object of research is studying Raman scattering technique, photoluminescence and some optical properties of silver nanoparticles created by eco-friendly technique which independent on a long time, effort, energy and high temperatures, and with the highest adsorption capacity in order to achieve a high inhibition to paralyze the activity of the bacterial wall, by achieving the highest surface plasmon resonance (SRR). Silver nanoparticles were prepared using Matricaria Flower extract. Characterization of silver nanoparticles and detection of their effectiveness against microbial using two types of bacteria (Escherichia Coli and Staphylococcus aureus ), these nanoparticles were measured using a number of measurements, X-ray diffrac

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Publication Date
Tue Dec 01 2009
Journal Name
Iraqi Journal Of Physics
Cutting of Fiber-Reinforced Plastics by a CW CO2 Laser
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In this work, a CW CO2 laser was used for cutting samples of the fiber-reinforced
plastics (FRP) of three different types of reinforcing material; aramide, glass and carbon.
Cutting process was investigated throughout the variation of some parameters of cutting
process and their effects on cutting quality as well as the effect of an inert gas exist in the
interaction region and finally using a mechanical chopper in order to enhance the cutting
quality. Results obtained explained the possibility to perform laser cutting with high
quality in these materials by good control of the parameters and conditions of the process.

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Publication Date
Mon Feb 01 2016
Journal Name
Journal Of Engineering
Torsional Resistance of Reinforced Concrete Girders with Web Openings
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In this study, a three dimensional finite element analysis was utilized to study the behavior of reinforced concrete T-
girders with and without web openings under pure torsion by using
ANSYS
APDL
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