geomechanical characterization of volcanic rocks using empirical systems and data mining techniques
Clicks: 238
ID: 142549
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
30.0
/100
238 views
27 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #29 of 40 articles by views in journal of neurotrauma
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
This paper tries to characterize volcanic rocks through the development and application of an empirical geomechanical system. Geotechnical information was collected from the samples from several Atlantic Ocean islands including Madeira, Azores and Canarias archipelagos. An empirical rock classification system termed as the volcanic rock system (VRS) is developed and presented in detail. Results using the VRS are compared with those obtained using the traditional rock mass rating (RMR) system. Data mining (DM) techniques are applied to a database of volcanic rock geomechanical information from the islands. Different algorithms were developed and consequently approaches were followed for predicting rock mass classes using the VRS and RMR classification systems. Finally, some conclusions are drawn with emphasis on the fact that a better performance was achieved using attributes from VRS.
| Reference Key |
miranda2018journalgeomechanical
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;T. Miranda;L.R. Sousa;A.T. Gomes;J. Tinoco;C. Ferreira |
| Journal | journal of neurotrauma |
| Year | 2018 |
| DOI |
10.1016/j.jrmge.2017.11.003
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.