Abstract

The conventional particle size test has been a widely used method in the characterization of soils and tailings. Such information is particularly useful in the evaluation of materials deposited in tailing stacks or compacted landfills, which must follow reference particle size ranges. However, the method has limitations, the main one being the execution time, which usually lasts around three days. On the other hand, laser testing appears as a viable alternative. This innovative method obtains the grain size curve of the soil through the light dispersion pattern and lasts a few minutes, a significant improvement over the conventional method. Furthermore, this method can cover particle size ranges of up to 0.1 micrometers, while the conventional method is limited to 1 micrometer. Despite the benefits of using this equipment, the laser grain size test does not yet have specific standardization for use in the field of soil mechanics. In this context, this work proposes the use of machine learning techniques to demonstrate the existence of compatibility between both methods. To this end, tests were carried out using both methodologies on different samples of iron ore tailings and an algorithm was developed to predict the material classification. The evaluation of the results made it possible to verify the consistency and precision of the results between the two methods, reinforcing the reliability and viability of the laser test as an efficient alternative to the traditional method

Abstract

The conventional particle size test has been a widely used method in the characterization of soils and tailings. Such information is particularly useful in the evaluation of materials deposited in tailing stacks or compacted landfills, which must follow reference particle size ranges. However, the method has limitations, the main one being the execution time, which usually lasts around three days. On the other hand, laser testing appears as a viable alternative. This innovative method obtains the grain size curve of the soil through the light dispersion pattern and lasts a few minutes, a significant improvement over the conventional method. Furthermore, this method can cover particle size ranges of up to 0.1 micrometers, while the conventional method is limited to 1 micrometer. Despite the benefits of using this equipment, the laser grain size test does not yet have specific standardization for use in the field of soil mechanics. In this context, this work proposes the use of machine learning techniques to demonstrate the existence of compatibility between both methods. To this end, tests were carried out using both methodologies on different samples of iron ore tailings and an algorithm was developed to predict the material classification. The evaluation of the results made it possible to verify the consistency and precision of the results between the two methods, reinforcing the reliability and viability of the laser test as an efficient alternative to the traditional method

Abstract

Reprocessing old tailings storage facilities (TSFs) has become increasingly common in the past ten years because of economic, environmental, and social reasons. Tailings deposited by spigots experience segregation and layering, creating deposits that are difficult to excavate due to the highly erratic geotechnical behaviour of the exposed faces. Both in-situ and laboratory testing are necessary to understand how steep, temporary tailings slopes might behave, ensuring stability through engineering analysis. This paper describes a detailed geotechnical characterization of an old TSF impoundment by in situ and laboratory testing, including sonic drilling, SCPTu soundings, geophysical field testing, and oedometric, monotonic and cyclic triaxial lab testing. Two different areas were surveyed: tailings near the dam, where coarser material is expected, and in the centre of the impoundment for the characterization of finer materials. We focused on the critical state behaviour of tailings and estimations of the state parameter, required to calibrate the numerical models employed in the analyses.