Rock mass quality classification based on deep learning: A feasibility study for stacked autoencoders
Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable stability assessment. To develop a tool that can deliver quick and accurate evaluation of rock mass quality, a deep learning approach is developed, which uses stacked autoencoders (SAEs) with sever...
Main Authors: | Danjie Sheng, Jin Yu, Fei Tan, Defu Tong, Tianjun Yan, Jiahe Lv |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-07-01
|
Series: | Journal of Rock Mechanics and Geotechnical Engineering |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1674775522001834 |
Similar Items
-
Enhancing the Visibility of Delamination during Pulsed Thermography of Carbon Fiber-Reinforced Plates Using a Stacked Autoencoder
by: Changhang Xu, et al.
Published: (2018-08-01) -
Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients
by: Yanzhen Wang, et al.
Published: (2019-09-01) -
Detection of sea‐surface target of coastal defense radar based on Stacked Autoencoder (SAE) algorithm
by: He Yan, et al.
Published: (2022-02-01) -
A Fault Identification Method for Metal Oxide Arresters Combining Suppression of Environmental Temperature and Humidity Interference with a Stacked Autoencoder
by: Shengwen Shu, et al.
Published: (2023-12-01) -
SAR Target Recognition with Feature Fusion Based on Stacked Autoencoder
by: Kang Miao, et al.
Published: (2017-04-01)