Chile is one of the main copper producers in the world. It is located in a geographical area where mega-earthquakes occur and this fact, together with the development of larger and higher sand tailings dams (with some facilities currently under development having final heights in excess of 250 m), requires that careful attention be paid to the safety and security of these facilities. In this paper, the main failure mechanisms of these sand tailings dams that have generated incidents of different magnitude involving loss of human life, significant environmental damage, and economic losses are described. Some key characteristics of reported incidents in Chile are presented, including failures resulting from the mega-earthquake that occurred on 27 February 2010 (Maule Region, Chile). Finally, the engineering practice and present Chilean regulatory framework, which have allowed progressive improvements in the construction, operation, and closure of such deposits, are described.Résumé : Le Chili est un des premiers producteurs de cuivre du monde. Ce pays est situé dans une zone géographique où de nombreux tremblements de terre de forte magnitude se produisent régulièrement, ce qui nécessite de porter une attention particulière à la sécurité et à la fiabilité des barrages de résidus miniers qui du fait de la croissance actuelle de l'industrie du cuivre sont de plus en plus importants et hauts (certains peuvent atteindre jusqu'à 250 m de hauteur). Dans cet article, les principaux mécanismes de rupture de ces barrages ayant entrainés des pertes humaines, des dommages environnementaux et économiques, sont présentés. Des détails sur les incidents répertoriés au Chili sur ces ouvrages sont décrits et notamment ceux concernant le tremblement de terre exceptionnel qui s'est déroulé le 27 février 2010 dans la région de Maule. Finalement, nous présentons le cadre réglementaire chilien actuel et les pratiques d'ingénieries mises en place en vue d'améliorer progressivement la construction, le fonctionnement opérationnel et la fermeture de ces ouvrages.Mots-clés : barrages de résidus miniers, tremblements de terre, liquéfaction, stabilité de pente.
Sand tailings dams have historically been the most commonly used technology for tailings storage in Chile. Although engineering advances have resulted in the construction of approximately 250-m-high facilities, some operational challenges still remain, including compaction control. Control is currently performed at a few control points in a dam embankment, without considering a series of factors that affect its mechanical behavior (e.g.,layer thickness and material variability). Within this context, geostatistics can be applied in combination with low-cost geotechnical tools as an alternative to improve compaction control in tailings storage facilities. In this study, an extensive field investigation was carried out. A total of 91 PANDA penetrometer tests were conducted to monitor the degree of compaction in an experimental classified sand tailings dam. The results were analyzed using stochastic interpolation for ordinary kriging and considering the spatial distribution of the cone resistance and the degree of compaction determined for the dam. The results showed that spatial variability was associated with the material variability of sand tailings and the compaction method used, and deviations from design requirements. The article shows the value of the use of geostatistics in decision-making in the case of classified sand tailings dams. This is mainly due to the fact that it allows optimization of the compaction process used in these tailings dams. Additionally, a useful database is generated to continue deepening studies of physical stability during the useful life of the tailings storage facilities.
In this research, we address the problem of evaluating physical stability (PS) to close tailings dams (TD) from medium-sized Chilean mining using artificial intelligence (AI) algorithms. The PS can be analyzed through the study of critical variables of the TD that allow estimating different potential failure mechanisms (PFM): seismic liquefaction, slope instability, static liquefaction, overtopping, and piping, which may occur in this type of tailings storage facilities in a seismically active country such as Chile. Thus, this article proposes the use of four machine learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural networks (ANN), and extreme gradient boosting (XGBoost), to estimate five possible PFM. In addition, due to the scarcity of data to train the algorithms, the use of generative adversarial networks (GAN) is proposed to create synthetic data and increase the database used. Therefore, the novelty of this article consists in estimating the PFM for TD and generating synthetic data through the GAN. The results show that, when using the GAN, the result obtained by the ML models increases the F1-score metric by 30 percentage points, obtaining results of 97.4%, 96.3%, 96.7%, and 97.3% for RF, SVM, ANN, and XGBoost, respectively.
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