An experimental programme was undertaken to investigate the effect of scrap tyre rubber on the swelling behaviour of composite clayey soils, using a large mix ratio. Two soils were studied (Ayaida and bentonite soils in the north-west of Algeria) by considering the high compressibility and low water absorption of scrap tyre rubber. Grain size, specific gravity, Atterberg limit analysis, swell-consolidation and loading–unloading tests were performed on the two soils and their mixtures with varying fibre content (10%, 20%, 25% and 50%). The results show that the liquid limits, swell potentials, swelling pressure and time to reach maximum heave decrease gradually when the scrap rubber content increases, and this reduction is significant for the soil with the higher swelling potential. Owing to the high compressibility of scrap tyre rubber, the compression and recompression indexes increase considerably with the content of scrap tyre rubber. It appears from the results that scrap tyre rubber can be used as reinforcement material for the modification of clayey soils, yet with a content that should not greatly affect the mixture compressibility.
Scrap tires are abundant and alarming waste. The aggregates resulting from the crushing of the waste tires are more and more used in the field of civil engineering (geotechnical, hydraulic works, light concretes, asphaltic concretes, etc.). Depending on the type of the used tires, dimensions and possible separations and treatment, the physical and mechanical characteristics of these aggregates might change. Some physical, chemical and direct shear tests were performed on three gradation classes of waste tire rubber powder. The tests results were combined with data from previous studies to generate empirical relationships between cohesion, friction angle and particle size of waste tire powder rubber. A cubic (third order) regression model seems to be more appropriate compared to linear and quadratic models.
Artificial neural network (ANN) is a soft computing technique that has been used to predict with accuracy compressive strength known for its high variability of values. ANN is used to develop a model that can predict compressive strength of rubberized concrete where natural aggregate such as fine and coarse aggregate are replaced by crumb rubber and tire chips. The main idea in this study is to build a model using ANN with three parameters that are: water/cement ratio, Superplasticizer, granular squeleton. Furthermore, the data used in the model has been taken from various literatures and are arranged in a format of three input parameters: water/ cement ratio, superplasticizer, granular squeleton that gathers fine aggregates, coarse aggregates, crumb rubber, tire chips and output parameter which is compressive strength.The performance of the model has been judged by using correlation coefficient, mean square error, mean absolute error and adopted as the comparative measures against the experimental results obtained from literature. The results indicate that artificial neural network has the ability to predict compressive strength of rubberized concrete with an acceptable degree of accuracy using new parameters.
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