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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:

Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods
Renpeng Chen, Pin Zhang, Xin Kang, et al.
SOILS AND FOUNDATIONS (2019) Vol. 59, Iss. 2, pp. 284-295
Open Access | Times Cited: 216

Showing 1-25 of 216 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: 406
Application of artificial intelligence in geotechnical engineering: A state-of-the-art review
Abolfazl Baghbani, Tanveer Choudhury, Susanga Costa, et al.
Earth-Science Reviews (2022) Vol. 228, pp. 103991-103991
Closed Access | Times Cited: 198
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: 180
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: 176
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: 155
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: 101
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: 81
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: 74
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: 60
Physics-informed deep learning method for predicting tunnelling-induced ground deformations
Zilong Zhang, Qiujing Pan, Zihan Yang, et al.
Acta Geotechnica (2023) Vol. 18, Iss. 9, pp. 4957-4972
Closed Access | Times Cited: 55
Deep learning-based prediction of steady surface settlement due to shield tunnelling
Wang Gan, Qian Fang, Jianming Du, et al.
Automation in Construction (2023) Vol. 154, pp. 105006-105006
Closed Access | Times Cited: 46
Forecasting and early warning of shield tunnelling-induced ground collapse in rock-soil interface mixed ground using multivariate data fusion and Catastrophe Theory
Long-Chuan Deng, Wei Zhang, Lu Deng, et al.
Engineering Geology (2024) Vol. 335, pp. 107548-107548
Closed Access | Times Cited: 29
Risk assessment of shield tunneling crossing building based on variable weight theory and cloud model
Desai Guo, Fanyan Meng, Huai-Na Wu, et al.
Tunnelling and Underground Space Technology (2024) Vol. 145, pp. 105593-105593
Closed Access | Times Cited: 20
Prediction of tunnel deformation using PSO variant integrated with XGBoost and its TBM jamming application
Yin Bo, Xiaogang Guo, Quansheng Liu, et al.
Tunnelling and Underground Space Technology (2024) Vol. 150, pp. 105842-105842
Closed Access | Times Cited: 17
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: 129
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
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: 111
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: 108
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: 95
Forecasting maximum surface settlement caused by urban tunneling
Arsalan Mahmoodzadeh, Mokhtar Mohammadi, Ako Daraei, et al.
Automation in Construction (2020) Vol. 120, pp. 103375-103375
Closed Access | Times Cited: 94
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: 90
A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China
Xuedong Bai, Wen-Chieh Cheng, Ge Li
Acta Geotechnica (2021) Vol. 16, Iss. 12, pp. 4061-4080
Closed Access | Times Cited: 87
Bayesian neural network-based uncertainty modelling: application to soil compressibility and undrained shear strength prediction
Pin Zhang, Zhen‐Yu Yin, Yin‐Fu Jin
Canadian Geotechnical Journal (2021) Vol. 59, Iss. 4, pp. 546-557
Closed Access | Times Cited: 83
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: 77
Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils
Behnam Ghorbani, Arul Arulrajah, Guillermo A. Narsilio, et al.
SOILS AND FOUNDATIONS (2020) Vol. 60, Iss. 2, pp. 398-412
Open Access | Times Cited: 74
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