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 and Gullibert Novoa
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Fernando Pacheco: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
Gabriel Hermosilla: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
Osvaldo Piña: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
Gabriel Villavicencio: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
Héctor Allende-Cid: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2241, Chile
Juan Palma: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
Pamela Valenzuela: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
José García: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
Alex Carpanetti: Escuela de Ingeniería Química, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2162, Chile
Vinicius Minatogawa: Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil, Valparaíso 2147, Chile
Gonzalo Suazo: Departamento de Obras Civiles, Escuela de Ingeniería Civil, Universidad Técnica Federico Santa María, Avenida España, Valparaíso 1680, Chile
Andrés León: Servicio Nacional de Geología y Minería, Avenida Santa María, Santiago 0104, Chile
Ricardo López: Servicio Nacional de Geología y Minería, Avenida Santa María, Santiago 0104, Chile
Gullibert Novoa: Servicio Nacional de Geología y Minería, Avenida Santa María, Santiago 0104, Chile

Mathematics, 2022, vol. 10, issue 23, 1-15

Abstract: 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.

Keywords: tailings dams; physical stability; machine learning; data augmentation; generative adversarial networks; artificial neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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