Work

Artificial Intelligence Algorithm for Tailing Storage Facility Soil Classification Based on CPT Measurements

Year: 2023

Type: conference-paper

Abstract: Due to the high environmental risks and negative impact of a failure, tailings storage facilities (TSFs) need constant monitoring. Advanced mathematical models have been developed in the past to predict the behavior of TSFs and raise alerts if needed. To be precise and reliable, such models need a spatial distribution of soil types within the dam as an input. Getting this data from laboratory measurements is time and cost-consuming. In this article, we propose an ANN-powered algorithm, which allows us to accurately estimate the soil distribution based on a cone penetration test (CPT).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.8123733

Authors: Natalia, Duda-Mróz, Sergii, Anufriiev, Wioletta, Koperska, Paweł, Stefaniak, Paweł, Stefanek

Institutions: KGHM Cuprum (Poland), KGHM Polska Miedź (Poland)

Language: English

Topic: Tailings Management and Properties

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Horizon 2020 Framework Programme, EIT RawMaterials

Awards: illuMINEation --- Bright concepts for a safe and sustainable digital mining future, G434337789, G7920627862, G8570786748