An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
Rockburst is a serious hazard in underground engineering, and accurate prediction of rockburst risk is challenging. To construct an intelligent prediction model of rockburst risk with interpretability and high accuracy, three binary scorecards predicting different risk levels of rockburst were const...
Enregistré dans:
Auteurs principaux: | Honglei Wang, Zhenlei Li, Dazhao Song, Xueqiu He, Aleksei Sobolev, Majid Khan |
---|---|
Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/2c9aa19e99604b91a8cadceefc489b1a |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Ellinaite, CaCr<sub>2</sub>O<sub>4</sub>, a new natural post-spinel oxide from Hatrurim Basin, Israel, and Juína kimberlite field, Brazil
par: V. V. Sharygin, et autres
Publié: (2021) -
New data on gersdorffite and associated minerals from the Peloritani Mountains (Sicily, Italy)
par: D. Mauro, et autres
Publié: (2021) -
Geochronology of granites of the western Korosten AMCG complex (Ukrainian Shield): implications for the emplacement history and origin of miarolitic pegmatites
par: L. Shumlyanskyy, et autres
Publié: (2021) -
Characterization and origin of the Mn-rich patinas formed on Lunéville château sandstones
par: L. Gatuingt, et autres
Publié: (2021) -
New insights in the mechanisms of the reaction 3.65 Å phase = clinoenstatite + water down to nanoscales
par: M. Koch-Müller, et autres
Publié: (2021)