OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
Siguang Zhao, Mingnian Wang, Wenhao Yi, et al.
Applied Sciences (2022) Vol. 12, Iss. 5, pp. 2656-2656
Open Access | Times Cited: 23

Showing 23 citing articles:

Geological information prediction for shield machine using an enhanced multi-head self-attention convolution neural network with two-stage feature extraction
Chengjin Qin, Guoqiang Huang, Honggan Yu, et al.
Geoscience Frontiers (2022) Vol. 14, Iss. 2, pp. 101519-101519
Open Access | Times Cited: 43

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine
Kürşat Kiliç, Hajime Ikeda, Tsuyoshi Adachi, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 15, Iss. 11, pp. 2857-2867
Open Access | Times Cited: 26

Ore/waste identification in underground mining through geochemical calibration of drilling data using machine learning techniques
Alberto Fernández, Pablo Segarra, José A. Sanchidrián, et al.
Ore Geology Reviews (2024) Vol. 168, pp. 106045-106045
Open Access | Times Cited: 6

The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels
Junling Zhang, Min Mei, Jun Wang, et al.
Applied Sciences (2024) Vol. 14, Iss. 2, pp. 912-912
Open Access | Times Cited: 4

Rockburst prediction based on 3D spatial feature system of tunnel face drilling parameters
Wenhao Yi, Mingnian Wang, Qinyong Xia, et al.
Tunnelling and Underground Space Technology (2025) Vol. 159, pp. 106350-106350
Closed Access

Efficiency of Classification Algorithms for Prediction of Rock Mass ÖNORM B Class in Himalayan Tunnelling
Ashutosh Kainthola, Md Alquamar Azad, Abhishek Srivastav, et al.
Indian geotechnical journal (2025)
Closed Access

Prediction of jumbo drill penetration rate in underground mines using various machine learning approaches and traditional models
Sasan Heydari, Seyed Hadi Hoseinie, Raheb Bagherpour
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3

A COMPARATIVE STUDY ON MACHINE LEARNING APPROACHES FOR ROCK MASS CLASSIFICATION USING DRILLING DATA
Tom F. Hansen, Georg H. Erharter, Zhongqiang Liu, et al.
Applied Computing and Geosciences (2024) Vol. 24, pp. 100199-100199
Open Access | Times Cited: 3

Predicting Rock Unconfined Compressive Strength Based on Tunnel Face Boreholes Measurement-While-Drilling Data
Xuepeng Ling, Mingnian Wang, Wenhao Yi, et al.
KSCE Journal of Civil Engineering (2024) Vol. 28, Iss. 12, pp. 5946-5962
Closed Access | Times Cited: 2

Performance evaluation of hybrid YYPO-RF, BWOA-RF and SMA-RF models to predict plastic zones around underground powerhouse caverns
Jian Zhou, Yuxin Chen, Weixun Yong
Geomechanics and Geophysics for Geo-Energy and Geo-Resources (2022) Vol. 8, Iss. 6
Closed Access | Times Cited: 11

Research on a Multi-Objective Optimization Method for Transient Flow Oscillation in Multi-Stage Pressurized Pump Stations
Yuxiang Ding, Guiying Shen, Wuyi Wan
Water (2024) Vol. 16, Iss. 12, pp. 1728-1728
Open Access | Times Cited: 2

Hard-rock tunnel lithology identification using multi-scale dilated convolutional attention network based on tunnel face images
Wenjun Zhang, Wuqi Zhang, Gaole Zhang, et al.
Frontiers of Structural and Civil Engineering (2023) Vol. 17, Iss. 12, pp. 1796-1812
Closed Access | Times Cited: 4

Rockburst prediction based on multi-featured drilling parameters and extreme tree algorithm for full-section excavated tunnel faces
Wenhao Yi, Mingnian Wang, Qinyong Xia, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access | Times Cited: 1

AI-Powered Geotechnics: Enhancing Rock Mass Classification for Safer Engineering Practices
Saadati. Ghader, Sina Javankhoshdel, Javad Mohebbi Najm Abad, et al.
Rock Mechanics and Rock Engineering (2024)
Open Access | Times Cited: 1

A new, fast, and accurate algorithm for predicting soil slope stability based on sparrow search algorithm-back propagation
Binbin Zheng, Jiahe Wang, Shuhu Feng, et al.
Natural Hazards (2023)
Closed Access | Times Cited: 3

Investigating Urban Underground Space Suitability Evaluation Using Fuzzy C-Mean Clustering Algorithm—A Case Study of Huancui District, Weihai City
Minlei Wang, Hanxun Wang, Yan Feng, et al.
Applied Sciences (2022) Vol. 12, Iss. 23, pp. 12113-12113
Open Access | Times Cited: 5

Novel hybrid classification model for multi-class imbalanced lithology dataset
Eman Ibrahim Alyasin, Oğuz Ata, Hayder Mohammedqasim
Optik (2022) Vol. 270, pp. 170047-170047
Closed Access | Times Cited: 3

Intelligent Identification of Surrounding Rock Grades Based on a Self-Developed Rock Drilling Test System
Quanwei Liu, Junlong Yan, Hongzhao Li, et al.
Buildings (2024) Vol. 14, Iss. 7, pp. 2176-2176
Open Access

Towards Automated Lithology Classification in NATM Tunnel: A Data-Driven Solution for Multi-dimensional Imbalanced Data
Yang Li, Jiayao Chen, Qian Fang, et al.
Rock Mechanics and Rock Engineering (2024)
Closed Access

Semi-supervised method for tunnel blasting quality prediction using measurement while drilling data
Hengxiang Jin, Qian Fang, Jun Wang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access

The Proposal of a Method for Rock Classification Using a Vibration Signal Propagated during the Rotary Drilling Process
Beáta Stehlíková, Gabriela Bogdanovská, Patrik Flegner, et al.
Applied Sciences (2023) Vol. 13, Iss. 20, pp. 11315-11315
Open Access | Times Cited: 1

Prediction of Subway Vibration Values on the Ground Level Using Machine Learning
Miller Mark, Yong Fang, Hu Luo, et al.
Geotechnical and Geological Engineering (2023) Vol. 41, Iss. 6, pp. 3753-3766
Closed Access

Advanced Underground Space Technology
Chenjie Gong, Mingfeng Lei, Xianda Shen
Applied Sciences (2022) Vol. 12, Iss. 19, pp. 9613-9613
Open Access

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