Generation of Synthetic Data for the Analysis of the Physical Stability of Tailing Dams through Artificial Intelligence

Fernando Pacheco, Gabriel Hermosilla, Osvaldo Piña, Gabriel Villavicencio, Héctor Allende-Cid, Juan Palma, Pamela Valenzuela, José García, Alex Carpanetti, Vinicius Minatogawa, Gonzalo Suazo, Andrés León, Ricardo López, Gullibert Novoa

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article number4396
Issue number23
StatePublished - Dec 2022


  • artificial neural networks
  • data augmentation
  • generative adversarial networks
  • machine learning
  • physical stability
  • tailings dams


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