The primary objective of this study was to identify the mechanisms for the development and propagation of longitudinal cracks that initiate at the surface of composite pavement. In this study the finite element program ANSYS version (5.4) was used and the model worked out using this program has the ability to analyze a composite pavement structure of different layer properties. Also, the aim of this study was modeling and analyzing of the composite pavement structure with the physical presence of crack induced in concrete underlying layer. The results obtained indicates that increasing the thickness of the asphalt layer tends to decrease the stress intensity factor, which may be attributed to the rapidly decrease of horizontal tensile stress in the asphalt layer. The cracks initiate at the surface due to high vertical stress and shear stress from wheel loads tends to propagate downward due tensile stress generated at the bottom of the asphalt layer or near crack tip, and the whole process occur at the same location of the existing cracks in underlying concrete layer rather than travel up from existing crack. As the load position varies from the crack zone, this result in tensile stresses or tension at the crack tip, leading to increase the stress intensity factor and intern result in crack propagation further into the depth of the pavement.
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This research deals with the qualitative and quantitative interpretation of Bouguer gravity anomaly data for a region located to the SW of Qa’im City within Anbar province by using 2D- mapping methods. The gravity residual field obtained graphically by subtracting the Regional Gravity values from the values of the total Bouguer anomaly. The residual gravity field processed in order to reduce noise by applying the gradient operator and 1st directional derivatives filtering. This was helpful in assigning the locations of sudden variation in Gravity values. Such variations may be produced by subsurface faults, fractures, cavities or subsurface facies lateral variations limits. A major fault was predicted to extend with the direction NE-
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Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill thi
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