A flexible pavement structure usually comprises more than one asphalt layer, with varying thicknesses and properties, in order to carry the traffic smoothly and safely. It is easy to characterize each asphalt layer with different tests to give a full description of that layer; however, the performance of the whole; asphalt structure needs to be properly understood. Typically, pavement analysis is carried out using multi-layer linear elastic assumptions, via equations and computer programs such as KENPAVE, BISAR, etc. These types of analysis give the response parameters including stress, strain, and deflection at any point under the wheel load. This paper aims to estimate the equivalent Resilient Modulus (MR) of the asphalt concrete layers within a pavement structure by using their individual MR values. To achieve this aim, eight samples were cored from Iraqi Expressway no. 1; they had three layers of asphalt and were tested to obtain the MR of each core by using the uniaxial repeated loading test at 25 and 40 °C. The samples were then cut to separate each layer individually and tested for MR at the same testing temperatures; thus, a total of 60 resilient modulus tests were conducted. A new approach was introduced to estimate the equivalent MR as a function of the MR value for each layer. The results matched the values obtained by KENPAVE analysis.
In this paper, ceramic water filters were produced by using ten mixtures of different ratios of red clay and sawdust under different production conditions. The physical properties of these filters were tested. The production conditions include five press pressures ranged from 10 to 50MPa and a firing schedule having three different final temperatures of 1000, 1070, and 1100˚C. The tests results of the physical properties were used to obtain best compatibility between the hydraulic and the mechanical properties of these filters. Results showed that as the press pressure and the firing temperature are increased, the bulk density and the compressive and bending strengths of the produced filters are increased, while, the porosity and absorp
... Show MoreKE Sharquie, HA Hassan, AA Noaimi, IRAQI JOURNAL OF COMMUNITY MEDICINE, 2010
In this work 2-hydrazino pyrimidine (1) was prepared from 2-mercapto pyrimidine with hydrazine hydrate. Treatment of (1) with active methylene compounds gave 2-(3,5-dimethyl -1 H – Pyrazole-1-yl) pyrimidine , whereas the reaction of (1) with carboxylic anhydride namely maleic anhydride or 1,2,3,6-tetra hydro phthalic anhydride yielded 1-Pyrimidine-2-yl-1,2-dihydro pyridazine-3,6-dione (3) and 2 – Pyrimidin -2-yl -2,3,4 a ,5,8 a – hexahydro phthalazine 1,4 – dione (4) . Reaction of (1) with phenyl isothiocyanate and ethyl chloro acetate afforded 3-Phenyl-1,3-thiazolidine-2,4-dione-2( pyrimidine -2- yl hydrazone (6) Azomethine (7-10) were prepared through condensation of (1) with aromatic aldehydes or ketones, then comp
... Show MoreAbstract. Froth flotation is a solid-liquid separation technique that uses hydrophobicity as a driving force. Bacteria and other drinking water microorganisms tend to be hydrophobic and can be removed from water using this application. The biggest limitation against using froth flotation in the drinking water industry is the difficulty of producing froth without chemical frothers and holding bacteria in this froth without chemical collectors which deteriorate water taste and odor. Recently, researchers at the University of Sheffield described a method for producing froth using only water and compressed air. This has enabled froth flotation to be studied as an alternative to biocides for the removal of bacteria from drinking water. T
... Show MoreOne 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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