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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:

Real-time analysis and regulation of EPB shield steering using Random Forest
Pin Zhang, Renpeng Chen, Huai-Na Wu
Automation in Construction (2019) Vol. 106, pp. 102860-102860
Closed Access | Times Cited: 179

Showing 1-25 of 179 citing articles:

State-of-the-art review of soft computing applications in underground excavations
Wengang Zhang, Runhong Zhang, Chongzhi Wu, et al.
Geoscience Frontiers (2019) Vol. 11, Iss. 4, pp. 1095-1106
Open Access | Times Cited: 397
Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study
Pin Zhang, Huai-Na Wu, Renpeng Chen, et al.
Tunnelling and Underground Space Technology (2020) Vol. 99, pp. 103383-103383
Open Access | Times Cited: 173
Prediction of shield tunneling-induced ground settlement using machine learning techniques
Renpeng Chen, Pin Zhang, Huai-Na Wu, et al.
Frontiers of Structural and Civil Engineering (2019) Vol. 13, Iss. 6, pp. 1363-1378
Closed Access | Times Cited: 154
A novel hybrid surrogate intelligent model for creep index prediction based on particle swarm optimization and random forest
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin, et al.
Engineering Geology (2019) Vol. 265, pp. 105328-105328
Open Access | Times Cited: 154
State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction
S.C. Jong, Dominic Ek Leong Ong, Erwin Oh
Tunnelling and Underground Space Technology (2021) Vol. 113, pp. 103946-103946
Closed Access | Times Cited: 112
State-of-the-Art Review of Machine Learning Applications in Constitutive Modeling of Soils
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin
Archives of Computational Methods in Engineering (2021) Vol. 28, Iss. 5, pp. 3661-3686
Closed Access | Times Cited: 105
Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method
K. K. Pabodha M. Kannangara, Wan‐Huan Zhou, Zhi Ding, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 14, Iss. 4, pp. 1052-1063
Open Access | Times Cited: 94
Deep learning analysis for energy consumption of shield tunneling machine drive system
Khalid Elbaz, Tao Yan, Annan Zhou, et al.
Tunnelling and Underground Space Technology (2022) Vol. 123, pp. 104405-104405
Closed Access | Times Cited: 79
Surface settlement prediction for urban tunneling using machine learning algorithms with Bayesian optimization
Dongku Kim, Kibeom Kwon, Khanh Pham, et al.
Automation in Construction (2022) Vol. 140, pp. 104331-104331
Closed Access | Times Cited: 73
Digital twin enabled real-time advanced control of TBM operation using deep learning methods
Limao Zhang, Guo Jing, Xianlei Fu, et al.
Automation in Construction (2023) Vol. 158, pp. 105240-105240
Closed Access | Times Cited: 41
Adaptive VMD and multi-stage stabilized transformer-based long-distance forecasting for multiple shield machine tunneling parameters
Chengjin Qin, Guoqiang Huang, Honggan Yu, et al.
Automation in Construction (2024) Vol. 165, pp. 105563-105563
Closed Access | Times Cited: 22
Multisource information fusion for real-time prediction and multiobjective optimization of large-diameter slurry shield attitude
Xianguo Wu, Jingyi Wang, Zongbao Feng, et al.
Reliability Engineering & System Safety (2024) Vol. 250, pp. 110305-110305
Closed Access | Times Cited: 14
High performance prediction of soil compaction parameters using multi expression programming
Hanlin Wang, Zhen‐Yu Yin
Engineering Geology (2020) Vol. 276, pp. 105758-105758
Open Access | Times Cited: 128
A critical evaluation of machine learning and deep learning in shield-ground interaction prediction
Pin Zhang, Huai-Na Wu, Renpeng Chen, et al.
Tunnelling and Underground Space Technology (2020) Vol. 106, pp. 103593-103593
Closed Access | Times Cited: 124
A novel feature selection method based on global sensitivity analysis with application in machine learning-based prediction model
Pin Zhang
Applied Soft Computing (2019) Vol. 85, pp. 105859-105859
Closed Access | Times Cited: 115
Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia
Suraj Kumar Bhagat, Tran Minh Tung, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Journal of Hazardous Materials (2020) Vol. 403, pp. 123492-123492
Closed Access | Times Cited: 111
Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements
Kun Zhang, Hai‐Min Lyu, Shui‐Long Shen, et al.
Tunnelling and Underground Space Technology (2020) Vol. 106, pp. 103594-103594
Open Access | Times Cited: 109
Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 1, pp. 441-452
Open Access | Times Cited: 106
Three-dimensional stress-transfer mechanism and soil arching evolution induced by shield tunneling in sandy ground
Xing‐Tao Lin, Renpeng Chen, Huai-Na Wu, et al.
Tunnelling and Underground Space Technology (2019) Vol. 93, pp. 103104-103104
Closed Access | Times Cited: 89
An AI‐based model for describing cyclic characteristics of granular materials
Pin Zhang, Zhen‐Yu Yin, Yin‐Fu Jin, et al.
International Journal for Numerical and Analytical Methods in Geomechanics (2020) Vol. 44, Iss. 9, pp. 1315-1335
Open Access | Times Cited: 88
Prediction of lining response for twin tunnels constructed in anisotropic clay using machine learning techniques
Wengang Zhang, Yongqin Li, Chongzhi Wu, et al.
Underground Space (2020) Vol. 7, Iss. 1, pp. 122-133
Open Access | Times Cited: 87
Measurement and prediction of tunnelling-induced ground settlement in karst region by using expanding deep learning method
Ning Zhang, Annan Zhou, Yutao Pan, et al.
Measurement (2021) Vol. 183, pp. 109700-109700
Closed Access | Times Cited: 75
A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM
Pin Zhang, Zhen‐Yu Yin
Computer Methods in Applied Mechanics and Engineering (2021) Vol. 382, pp. 113858-113858
Open Access | Times Cited: 74
Real-time prediction of shield moving trajectory during tunnelling using GRU deep neural network
Nan Zhang, Ning Zhang, Qian Zheng, et al.
Acta Geotechnica (2021) Vol. 17, Iss. 4, pp. 1167-1182
Closed Access | Times Cited: 67
Machine learning-based automatic control of tunneling posture of shield machine
Hongwei Huang, Jiaqi Chang, Dongming Zhang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 14, Iss. 4, pp. 1153-1164
Open Access | Times Cited: 67
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