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Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
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Rutting is a crucial concern impacting asphalt concrete pavements’ stability and long-term performance, negatively affecting vehicle drivers’ comfort and safety. This research aims to evaluate the permanent deformation of pavement under different traffic and environmental conditions using an Artificial Neural Network (ANN) prediction model. The model was built based on the outcomes of an experimental uniaxial repeated loading test of 306 cylindrical specimens. Twelve independent variables representing the materials’ properties, mix design parameters, loading settings, and environmental conditions were implemented in the model, resulting in a total of 3214 data points. The network accomplished high prediction accuracy with an R2 of 0.93 and a mean squared error (MSE) of 0.0039. Results based on the sensitivity analysis and variable importance techniques showed that the percentage of aggregate passing the 4.75 mm sieve and the (rice) theoretical maximum specific gravity (Gmm) were the most significant factors in predicting axial permanent strain (εp). Furthermore, the connection weight method highlighted input variables’ distinct positive and negative impacts on permanent deformation.

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Publication Date
Sat Apr 07 2018
Journal Name
Civil Engineering Journal
Behavior of Precast Prestressed Concrete Segmental Beams
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The structural behavior of Segmental Precast Post-tensioned Reinforced Concrete (SPPRC) beams largely depends on the behavior of the joints that connect between the segments. In this research, series of static tests were carried out to investigate the behavior of full-scale SPPRC beams with different types of epoxy-glued joint configurations; multi-key joint, single key, and plain key joint. The reference specimen was monolithically casted beam and the other specimens were segmental beams with five segments for each one. The general theme from the experimental results reflects an approximate similarity in the behavior of the four beams with slight differences. Due to the high tensile strength of the used epoxy in comparison to concr

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Publication Date
Fri Jul 21 2023
Journal Name
Journal Of Engineering
Corrosion-Resistance Characteristics of Concrete Containing Furfural
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Corrosion inhibiting admixtures are unique among other methods to protect reinforced concrete from corrosion damage. In this study, the effect of furfural on the fresh and hardened properties of concrete mixes of 35 and 45 MPa compressive strengths as well as the corrosion inhibition of furfural was evaluated. Furfural was added at different dosages (1, 2 and 3% by weight of cement) with and without superplasticizer (HRWR). Different electrochemical measurements were performed (Half-cell potential, Tafel plot and linear polarization resistance). Electrochemical measurements confirmed that furfural dramatically reduces the rate of corrosion; the inhibition efficiencies were 62.7 and 63.8 % due to 3% furfural addition to 35 and 45MPa-concr

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Engineering
Some Durability Test of No-Fine Concrete
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In this study, two types of mixes were adopted by using two grading of coarse aggregate. The practical side of this study was to produce no-fine aggregate concrete by using crushed clay brick aggregates. The durability of the produced concrete and internal sulfate attack was studied.      For durability assessment, it is found that the no-fine concrete made with crushed brick aggregate lost about (15-25) % of its compressive strength after being subjected to 60 cycles of wetting and drying with age 120 days. The curing condition showed that the water curing improved the compressive strength with a rate higher than that when sealed or air dry curing were used. The crushed brick no-fine concrete de

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Publication Date
Thu Sep 01 2022
Journal Name
Engineering Structures
Mesoscale analysis of Fiber-Reinforced concrete beams
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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Comparative analysis of deep learning techniques for lung cancer identification
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One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

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Publication Date
Tue Mar 30 2021
Journal Name
Baghdad Science Journal
Application of Data Mining Techniques on Tourist Expenses in Malaysia
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Tourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective

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Publication Date
Wed Jan 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Numerical Investigation of Drag Reduction Techniques in a Car Model
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Abstract<p>Reducing the drag force has become one of the most important concerns in the automotive industry. This study concentrated on reducing drag through use of some external modifications of passive flow control, such as vortex generators, rear under body diffuser slices and a rear wing spoiler. The study was performed at inlet velocity (V=10,20,30,40 m/s) which correspond to an incompressible car model length Reynolds numbers (Re=2.62×10<sup>5</sup>, 5.23×10<sup>5</sup>, 7.85×10<sup>5</sup> and 10.46×10<sup>5</sup>), respectively and we studied their effect on the drag force. We also present a theoretical study finite volume method (FVM) of solvi</p> ... Show More
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Publication Date
Sun Jan 01 2017
Journal Name
Journal Of Engineering
Detection and Diagnosis of Induction Motor Faults by Intelligent Techniques
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This paper presents a complete design and implementation of a monitoring system for the operation of the three-phase induction motors. This system is built using a personal computer and  two types of sensors (current, vibration) to detect some of the mechanical faults that may occur in the motor. The study and examination of several types of faults including (ball bearing and shaft misalignment faults) have been done through the extraction of fault data by using fast Fourier transform (FFT) technique. Results showed that the motor current signature analysis (MCSA) technique, and measurement of vibration technique have high possibility in the detection and diagnosis of most mechanical faults with high accuracy. Subsequently, diagnosi

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Publication Date
Wed Apr 30 2025
Journal Name
Iraqi Journal Of Science
Numerical Simulation of Solar Granulation Dynamics Using Optical Correction Techniques
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High-resolution imaging of celestial bodies, especially the sun, is essential for understanding dynamic phenomena and surface details. However, the Earth's atmospheric turbulence distorts the incoming light wavefront, which poses a challenge for accurate solar imaging. Solar granulation, the formation of granules and intergranular lanes on the sun's surface, is important for studying solar activity. This paper investigates the impact of atmospheric turbulence-induced wavefront distortions on solar granule imaging and evaluates, both visually and statistically, the effectiveness of Zonal Adaptive Optics (AO) systems in correcting these distortions. Utilizing cellular automata for granulation modelling and Zonal AO correction methods,

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Publication Date
Sun Jul 31 2022
Journal Name
Iraqi Geological Journal
A Review of Historical Studies for Water Saturation Determination Techniques
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Water saturation is the most significant characteristic for reservoir characterization in order to assess oil reserves; this paper reviewed the concepts and applications of both classic and new approaches to determine water saturation. so, this work guides the reader to realize and distinguish between various strategies to obtain an appropriate water saturation value from electrical logging in both resistivity and dielectric has been studied, and the most well-known models in clean and shaly formation have been demonstrated. The Nuclear Magnetic Resonance in conventional and nonconventional reservoirs has been reviewed and understood as the major feature of this approach to estimate Water Saturation based on T2 distribution. Artific

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