In this study, composite materials were prepared using unsaturated polyester resin as binder with two types of fillers (sawdust and chopped reeds). The molding method is used to prepare sheets of UPE / sawdust composite and UPE / chopped reeds composite. The mechanical properties were studied including flexural strength and Young's modulus for the samples at normal conditions (N.C). The Commercial wood, UPE and its composite samples were immersed in water for about 30 days to find the weight gain (Mt%) of water for the samples, also to find the effect of water on their flexural strength and Young's modulus. The results showed that the samples of UPE / chopped reeds composite gained highest values of flexural strength (24.5 MPa) and Young's modulus (5.1 GPa) as compared with other composites at (N.C). The results showed that the wet samples of sawdust composite have lowest values of weight gain (Mt %) of water (0.043%) as compared with other composites after immersion. Also it’s showed a slight decrease in values of Young's modulus for all the samples after immersion as compared with the samples at (N.C). Finally it’s showed a slight decrease in values of flexural strength for all the samples except for the composite material formed from UPE / chopped reeds which showed an increase in the value of flexural strength after immersion, where the wet samples of UPE / chopped reeds composite gained (29 MPa) as compared with the samples at (N.C).
الخلاصة
تشير نظريات علم اجتماع المعرفة الى ان ظهور مفهوم معين او سقوطه لايتم بمحض الصدفة، بل نتيجة تفاعل مجموعة من العوامل الثقافية والسياسية وحتى الاقتصادية ومن هنا فأن ظهور مفهوم الاثنية كان محصلة عوامل كثيرة خصوصاً بعد ان اصبح موضوع الاثنية في العقود الاخيرة من القرن الماضي وبداية القرن الواحد والعشرين محور نقاش رئيس في ميدان البحث الاجتماعي والسياسي، ليس في
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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 R
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