Low bearing capacity of weak soil under shallow footings represents one of construction problems.
Kaolin with water content converges to liquid limit used to represent the weak soil under shallow
footing prototype. On the other hand, fly ash, which can be defined as undesirable industrial waste
material, was used to improve the bearing capacity of the soft soil considered in this research. The soft
soil was prepared in steel box (36×36×25) cm and shallow square footing prototype (6×6) cm were
used .Group of physical and chemical tests were conducted on kaolin and fly ash. The soft soil was
improved by a bed of compacted fly ash placed under the footing with dimensions equal to that of
footing but with different depth ratios. The results show that there is a noticeable improvement in the
behavior of footing when improved by compacted fly ash. The improvement showed a decrease in
settlement and increase in bearing capacity. The improvement ratio in bearing capacity was calculated
by comparing the ultimate bearing capacity value when testing the kaolin alone with its value of kaolin
improved with compacted fly ash at the same value of eccentricity. It is important to note that
eccentricity values were chosen according to the rule of middle third of footing base(i.e.,e≤B/6). The
improvement ratio was about (130%) in average value, that represent a good ratio of improvement
Singapore has invested its human resources for the development of the country since earlier times, so it has directed attention to the education sector. Education and development complement each other, as investment in education has contributed over several years to the development of Singapore economy and has made it one of the strongest economies in Asia and the world. Despite the obstacles that faced educational policies in Singapore, the presence of a wise leadership capable of overcoming difficulties and solving them, and providing the appropriate atmosphere for the development of education has contributed significantly to making it one of the most advanced educational systems in the world, and making this system the main pillar for
... Show MoreThe main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model
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Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM
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