Journal of Engineering Geology

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 In this research, prediction of concrete strength containing different aggregates using Non-destructive (Ultrasonic) testing through Artificial Neural Networks was carried out. For this purpose, aggregates with different properties were selected from the quarries, and then their destructive and nondestructive properties were obtained in laboratory. The significance of this research, using different aggregates with physical, mechanical and chemical properties also used two different test methods, such as Non-destructive static and dynamic testing, which are respectively uniaxial compressive strength and compressive wave velocity. Thus, this model includes various types of samples and the prediction model includes static and dynamic tests. The results showed that the use of artificial neural networks not only increases the accuracy, but also it reduces costs and time.

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Materials such as waste tire chips were widely used to improve the strength of soil. The objective of this study is to discuss the residual strength or steady-state behavior of sand-waste tire chip mixtures. A series of undrained monotonic triaxial compression tests were conducted on reconstituted saturated specimens of sand and sand-tire chip mixtures with variation in the tire-chip contents from 0 to 4 percentages by dry weight of soil. The specimens are prepared using dry deposition method of preparation. The influence on residual resistance of varying confining pressure (100, 200, and 300 kPa) and sand mixture relative density (40, 65, and 80%) were evaluated. Tests results showed that by increasing the tire chip contents, the residual strength increased and steady-state lines move to the right of log S_us-e diagram. Also, the residual resistance improvement induced by tire chip inclusions was found to be sensitive to the relative density of samples and applied confining pressure.