Predicting the stability of open stopes using Machine Learning

• Autorzy: Szmigiel Alicja , Apel Derek B.

Identyfikatory

DOI

10.46873/2300-3960.1369

• Języki publikacji: EN

Abstrakty

EN

The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database that consisted of 176 historical case studies was passed to the two most popular Machine Learning algorithms: Logistic Regression and Random Forest, to compare their predicting capabilities. The results obtained showed that those algorithms can indicate the stability of underground openings, especially Random Forest, which, in examined data, performed slightly better than Logistic Regression.

Słowa kluczowe

EN

open stope; machine learning; logistic regression; random forest

PL

system otwartych komór; uczenie maszynowe; regresja logistyczna; las losowy

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2022
• Tom: Vol. 21, iss. 3
• Strony: 241--248
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Szmigiel Alicja

• University of Alberta, School of Mining and Petroleum Engineering, Edmonton, Alberta T6G 2R3, Canada

• https://orcid.org/0000-0001-6770-0421

autor

Apel Derek B.

• University of Alberta, School of Mining and Petroleum Engineering, Edmonton, Alberta T6G 2R3, Canada

• https://orcid.org/0000-0002-5402-7036

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).