- Home
- Publications
- Publication Search
- Publication Details
Title
Use of machine learning for classification of sand particles
Authors
Keywords
-
Journal
Acta Geotechnica
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-04-10
DOI
10.1007/s11440-021-01443-y
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Comparison of 2D and 3D dynamic image analysis for characterization of natural sands
- (2021) Linzhu Li et al. ENGINEERING GEOLOGY
- Influence of particle size on the drained shear behavior of a dense fluvial sand
- (2021) Yibing Deng et al. Acta Geotechnica
- How to classify sand types: A deep learning approach
- (2021) Yejin Kim et al. ENGINEERING GEOLOGY
- Evaluation of Roundness Parameters in Use for Sand
- (2021) Linzhu Li et al. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING
- Assessment of rockburst risk using multivariate adaptive regression splines and deep forest model
- (2021) Deping Guo et al. Acta Geotechnica
- Machine learning reveals the influences of grain morphology on grain crushing strength
- (2021) Yihan Wang et al. Acta Geotechnica
- Modelling the mechanical behaviour of soils using machine learning algorithms with explicit formulations
- (2021) Pin Zhang et al. Acta Geotechnica
- Efficacy of 3D dynamic image analysis for characterising the morphology of natural sands
- (2021) Linzhu Li et al. GEOTECHNIQUE
- Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
- (2020) Wengang Zhang et al. Geoscience Frontiers
- Modelling of shallow landslides with machine learning algorithms
- (2020) Zhongqiang Liu et al. Geoscience Frontiers
- Granulometry of Two Marine Calcareous Sands
- (2020) Linzhu Li et al. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING
- Machine learning for data-driven discovery in solid Earth geoscience
- (2019) Karianne J. Bergen et al. SCIENCE
- Mineral grains recognition using computer vision and machine learning
- (2019) Julien Maitre et al. COMPUTERS & GEOSCIENCES
- Volumetric Particle Size Distribution and Variable Granular Density Soils
- (2019) Ryan D. Beemer et al. GEOTECHNICAL TESTING JOURNAL
- Minimum image quality for reliable optical characterizations of soil particle shapes
- (2019) Quan Sun et al. COMPUTERS AND GEOTECHNICS
- Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data
- (2019) Huizhen Hao et al. MINERALS ENGINEERING
- A multi-task multi-class learning method for automatic identification of heavy minerals from river sand
- (2019) Na Li et al. COMPUTERS & GEOSCIENCES
- Evolution of particle damage of sand during axial compression via arrested tests
- (2019) Eduardo Suescun-Florez et al. Acta Geotechnica
- Reliability and applicability of the Krumbein-Sloss chart for estimating geomechanical properties in sands
- (2018) Yejin Kim et al. ENGINEERING GEOLOGY
- Machine learning applications in minerals processing: A review
- (2018) J.T. McCoy et al. MINERALS ENGINEERING
- Fast Gaussian Naïve Bayes for searchlight classification analysis
- (2017) Marlis Ontivero-Ortega et al. NEUROIMAGE
- Particle Roundness and Sphericity from Images of Assemblies by Chart Estimates and Computer Methods
- (2016) Roman D. Hryciw et al. JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING
- Machine learning tools formineral recognition and classification from Raman spectroscopy
- (2015) C. Carey et al. JOURNAL OF RAMAN SPECTROSCOPY
- Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information
- (2013) Matthew J. Cracknell et al. COMPUTERS & GEOSCIENCES
- Influence of relative density, particle shape, and stress path on the plane strain compression behavior of granular materials
- (2013) Li Zhuang et al. Acta Geotechnica
- Estimation of prediction error by using K-fold cross-validation
- (2009) Tadayoshi Fushiki STATISTICS AND COMPUTING
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started