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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 Dynamic Earth-Pressure Regulation Model for Shield Tunneling by Integrating GRU Deep Learning Method With GA Optimization
Min-Yu Gao, Ning Zhang, Shui‐Long Shen, et al.
IEEE Access (2020) Vol. 8, pp. 64310-64323
Open Access | Times Cited: 62

Showing 1-25 of 62 citing articles:

Application of deep learning algorithms in geotechnical engineering: a short critical review
Wengang Zhang, Hongrui Li, Yongqin Li, et al.
Artificial Intelligence Review (2021) Vol. 54, Iss. 8, pp. 5633-5673
Closed Access | Times Cited: 391
Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods
Song-Shun Lin, Shui‐Long Shen, Annan Zhou, et al.
Automation in Construction (2020) Vol. 122, pp. 103490-103490
Closed Access | Times Cited: 190
Deep learning technologies for shield tunneling: Challenges and opportunities
Cheng Zhou, Yuyue Gao, Elton J. Chen, et al.
Automation in Construction (2023) Vol. 154, pp. 104982-104982
Closed Access | Times Cited: 54
Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling
Qing Kang, Elton J. Chen, Zhongchao Li, et al.
Underground Space (2023) Vol. 13, pp. 335-350
Open Access | Times Cited: 53
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
Data-driven joint multiobjective prediction and optimization for tunnel-induced adjacent bridge pier displacement: A case study in China
Hongyu Chen, Jun Liu, Yawei Qin, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 142, pp. 109616-109616
Closed Access | Times Cited: 1
Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning
Shui‐Long Shen, Pierre Guy Atangana Njock, Annan Zhou, et al.
Acta Geotechnica (2020) Vol. 16, Iss. 1, pp. 303-315
Closed Access | Times Cited: 134
Application of LSTM approach for modelling stress–strain behaviour of soil
Ning Zhang, Shui‐Long Shen, Annan Zhou, et al.
Applied Soft Computing (2020) Vol. 100, pp. 106959-106959
Closed Access | Times Cited: 120
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
Modelling the performance of EPB shield tunnelling using machine and deep learning algorithms
Song-Shun Lin, Shui‐Long Shen, Ning Zhang, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 5, pp. 101177-101177
Open Access | Times Cited: 78
Excess pore water pressure caused by the installation of jet grouting columns in clay
Zhi-Feng Wang, Shui‐Long Shen, Giuseppe Modoni, et al.
Computers and Geotechnics (2020) Vol. 125, pp. 103667-103667
Closed Access | Times Cited: 75
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
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
Dynamic prediction of mechanized shield tunneling performance
Ruohan Wang, Dianqing Li, Elton J. Chen, et al.
Automation in Construction (2021) Vol. 132, pp. 103958-103958
Closed Access | Times Cited: 66
Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization
Eren Baş, Erol Eǧrioǧlu, Emine Kolemen
Granular Computing (2021) Vol. 7, Iss. 2, pp. 411-420
Closed Access | Times Cited: 61
Distribution characteristics and utilization of shallow geothermal energy in China
Ye‐Shuang Xu, Xuwei Wang, Shui‐Long Shen, et al.
Energy and Buildings (2020) Vol. 229, pp. 110479-110479
Closed Access | Times Cited: 68
Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network
Song-Shun Lin, Shui‐Long Shen, Annan Zhou
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 14, Iss. 4, pp. 1232-1240
Open Access | Times Cited: 33
Data-driven multi-step robust prediction of TBM attitude using a hybrid deep learning approach
Kunyu Wang, Xianguo Wu, Limao Zhang, et al.
Advanced Engineering Informatics (2022) Vol. 55, pp. 101854-101854
Closed Access | Times Cited: 33
Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review
Feng Shan, Xuzhen He, Haoding Xu, et al.
Eng—Advances in Engineering (2023) Vol. 4, Iss. 2, pp. 1516-1535
Open Access | Times Cited: 16
Machine learning–informed soil conditioning for mechanized shield tunneling
Shuying Wang, Xiao Yuan, Tongming Qu
Computer-Aided Civil and Infrastructure Engineering (2024) Vol. 39, Iss. 17, pp. 2662-2682
Closed Access | Times Cited: 7
Fuzzy-metaheuristic ensembles for predicting the compressive strength of brick aggregate concrete
Wafaa Mohamed Shaban, Jian Yang, Khalid Elbaz, et al.
Resources Conservation and Recycling (2021) Vol. 169, pp. 105443-105443
Closed Access | Times Cited: 35
Enhancing earth pressure balance tunnel boring machine performance with support vector regression and particle swarm optimization
Hongjie Yu, Xu Zhou, Xiaoli Zhang, et al.
Automation in Construction (2022) Vol. 142, pp. 104457-104457
Closed Access | Times Cited: 24
Real-time forecasting of TBM cutterhead torque and thrust force using aware-context recurrent neural networks
Feng Shan, Xuzhen He, Danial Jahed Armaghani, et al.
Tunnelling and Underground Space Technology (2024) Vol. 152, pp. 105906-105906
Open Access | Times Cited: 4
Machine Learning-Based Measurement and Prediction of Ground Settlement Induced by Shield Tunneling Undercrossing Existing Tunnels in Composite Strata
Mei Dong, Min‐Xin Guan, Kuihua Wang, et al.
Sensors (2025) Vol. 25, Iss. 5, pp. 1600-1600
Open Access
Machine learning applications in designing cementitious materials
Shichen Dang, Hu Fang, Yao Yao
Automation in Construction (2025) Vol. 174, pp. 106125-106125
Closed Access
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