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Description
The mining industry requires solving a wide range of geotechnical problems, many of which involve large-scale particles and coarse materials, such as heap leach piles, spent ore dumps, ROM piles, open pits, and foundation material.
Over the years, the industry has relied on large-scale laboratory testing, geophysics, and in-situ testing; however, there does not appear to be a single method that consistently dominates practice, as is the case with CPTu for tailings.
Pressuremeters can test a large volume of material and have been used worldwide for over 70 years. Nevertheless, its full potential has been limited by traditional interpretation methods. Today, with the aid of advanced numerical modelling and artificial intelligence, this limitation can be overcome.
This work presents the use of pressuremeter tests to characterise a 40 m-high spent ore dump in northern Chile. The tests are back-analysed using advanced constitutive models and an AI-assisted software tool (DAARWIN) to match numerical predictions with measured responses. The resulting soil parameters are then used to model the structure, and the predictions are compared against field monitoring data, SCPTu tests, and large-scale triaxial testing results.