- Tytuł:
- Predicting the stability of open stopes using Machine Learning
- Autorzy:
-
Szmigiel, Alicja
Apel, Derek B. - Powiązania:
- https://bibliotekanauki.pl/articles/2201415.pdf
- Data publikacji:
- 2022
- Wydawca:
- Główny Instytut Górnictwa
- Tematy:
-
open stope
machine learning
logistic regression
random forest
system otwartych komór
uczenie maszynowe
regresja logistyczna
las losowy - Opis:
- 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.
- Źródło:
-
Journal of Sustainable Mining; 2022, 21, 3; 241--248
2300-1364
2300-3960 - Pojawia się w:
- Journal of Sustainable Mining
- Dostawca treści:
- Biblioteka Nauki
Artykuł