Failure risk of brazilian tailings dams: a data mining approach

Abstract

This paper proposes the use of a hybrid method that combines Biased Random Key Genetic Algorithm (BRKGA) with a local search heuristic to separate Brazilian tailing dam data into groups. The goal was identifying dams similar to Fundão and B1 failed dams. The groups were created by solving the clustering problem by BRKGA. The clustering problem consists in separating a set of objects into groups such that members of each group are similar to each other. The data was composed by 427 dams, with the actual 425 dams of Brazilian Register of Tailing Dams and the two Brazilian failed dams from the last years. Computational experiments considering real data available are presented to demonstrate the effi cacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identifi cation of tailings dams with risk potentials, assisting in the identifi cation of these dams.

Building pathologies caused by failure of Fundão Tailing Dam: A principal component analysis aproach

“…The dataset was analyzed using a unsupervised machine learning, named Principal Component Analysis (PCA). Machine learning is largely used for data analysis and these tools started to be used recently in civil and mining engineering (Santos et al 2018, Ali et al 2019, Coelho et al 2016, Kang et al 2021, Qi et al 2019, Paulo et al 2020, Yang et al 2021, Santos & Oliveira 2021.…”