Mechanical properties and time-dependent behaviour of sand-granulated rubber mixtures

The use of granulated recycled rubber as a lightweight material in civil engineering applications has been widely growing over the past 20 years. Processed waste tires mixed with soils have been introduced as lightweight fills for slopes, retaining walls, and embankments. It has also been considered as a damping material under foundations in seismic zones. Understanding the properties of sand-rubber mixtures is essential to evaluate its performance in geotechnical applications. Isotopically consolidated drained (CD) triaxial tests were conducted to investigate the effect of rubber size, content and saturation condition on the mechanical properties of sand-rubber mixtures. Moreover, the compressibility of the sand-rubber mixtures under sustained loading was investigated through one dimensional consolidation tests. The unit weight, shear strength and stiffness of sand-rubber mixtures decreased whereas deformability increased at increased rubber content. A non-linear stress-strain response was observed, that changed from brittle to ductile behaviour at increased rubber content. Sand-rubber mixtures, under one dimensional loading, exhibited significant settlement that increased as rubber content increased.     

Support vector machines applied to uniaxial compressive strength prediction of jet grouting columns

Learning from data is a very attractive alternative to “manually” learning. Therefore, in the last decade the use of machine learning has spread rapidly throughout computer science and beyond. This approach, supported on advanced statistics analysis, is usually known as Data Mining (DM) and has been applied successfully in different knowledge domains. In the present study, we show that DM can make a great contribution in solving complex problems in civil engineering, namely in the field of geotechnical engineering. Particularly, the high learning capabilities of Support Vector Machines (SVMs) algorithm, characterized by it flexibility and non-linear capabilities, were applied in the prediction of the Uniaxial Compressive Strength (UCS) of Jet Grouting (JG) samples directly extracted from JG columns, usually known as soilcrete. JG technology is a soft-soil improvement method worldwide applied, extremely versatile and economically attractive when compared with other methods. However, even after many years of experience still lacks of accurate methods for JG columns design. Accordingly, in the present paper a novel approach (based on SVM algorithm) for UCS prediction of soilcrete mixtures is proposed supported on 472 results collected from different geotechnical works. Furthermore, a global sensitivity analysis is applied in order to explain and extract understandable knowledge from the proposed model. Such analysis allows one to identify the key variables in UCS prediction and to measure its effect. Finally, a tentative step toward a development of UCS prediction based on laboratory studies is presented and discussed.