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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:

Applying Optimized Support Vector Regression Models for Prediction of Tunnel Boring Machine Performance
Hadi Fattahi, Nima Babanouri
Geotechnical and Geological Engineering (2017) Vol. 35, Iss. 5, pp. 2205-2217
Closed Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data
Quansheng Liu, Xinyu Wang, Xing Huang, et al.
Tunnelling and Underground Space Technology (2020) Vol. 106, pp. 103595-103595
Closed Access | Times Cited: 163
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: 114
Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach
Yue Pan, Xianlei Fu, Limao Zhang
Automation in Construction (2022) Vol. 141, pp. 104386-104386
Closed Access | Times Cited: 74
A LightGBM-based strategy to predict tunnel rockmass class from TBM construction data for building control
Long Li, Zaobao Liu, Ji-Mei Shen, et al.
Advanced Engineering Informatics (2023) Vol. 58, pp. 102130-102130
Closed Access | Times Cited: 45
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: 43
Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data
Sun We, Maolin Shi, Chao Zhang, et al.
Automation in Construction (2018) Vol. 92, pp. 23-34
Closed Access | Times Cited: 155
TBM performance prediction with Bayesian optimization and automated machine learning
Qianli Zhang, Weifei Hu, Zhenyu Liu, et al.
Tunnelling and Underground Space Technology (2020) Vol. 103, pp. 103493-103493
Closed Access | Times Cited: 95
Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning
Shangxin Feng, Zuyu Chen, Hua Luo, et al.
Tunnelling and Underground Space Technology (2021) Vol. 110, pp. 103636-103636
Closed Access | Times Cited: 91
TBM penetration rate prediction based on the long short-term memory neural network
Boyang Gao, RuiRui Wang, Chunjin Lin, et al.
Underground Space (2020) Vol. 6, Iss. 6, pp. 718-731
Open Access | Times Cited: 81
Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach
Xianlei Fu, Limao Zhang
Automation in Construction (2021) Vol. 132, pp. 103937-103937
Closed Access | Times Cited: 70
Feasibility of Intelligent Models for Prediction of Utilization Factor of TBM
Alireza Mohammadi Noori, Reza Mikaeil, Mojtaba Mokhtarian, et al.
Geotechnical and Geological Engineering (2020) Vol. 38, Iss. 3, pp. 3125-3143
Closed Access | Times Cited: 55
Transfer learning for collapse warning in TBM tunneling using databases in China
Jinhui Li, Dong Guo, Zuyu Chen, et al.
Computers and Geotechnics (2023) Vol. 166, pp. 105968-105968
Closed Access | Times Cited: 19
Data-driven joint multi-objective prediction and optimization for advanced control during tunnel construction
Xianlei Fu, Maozhi Wu, Robert Lee Kong Tiong, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 122118-122118
Closed Access | Times Cited: 17
Machine learning to inform tunnelling operations: recent advances and future trends
Brian Sheil, Stephen K. Suryasentana, Michael A. Mooney, et al.
Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction (2020) Vol. 173, Iss. 4, pp. 74-95
Open Access | Times Cited: 47
Interpretable predictive model for shield attitude control performance based on XGboost and SHAP
Min Hu, Haolan Zhang, Bingjian Wu, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 26
Data-driven estimation of TBM performance in soft soils using density-based spatial clustering and random forest
Xianlei Fu, Liuyang Feng, Limao Zhang
Applied Soft Computing (2022) Vol. 120, pp. 108686-108686
Closed Access | Times Cited: 25
Prediction of rock drillability using gray wolf optimization and teaching–learning-based optimization techniques
Hadi Fattahi, Hossein Ghaedi, Farshad Malekmahmoodi
Soft Computing (2023) Vol. 28, Iss. 1, pp. 461-476
Closed Access | Times Cited: 14
Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data
Behnam Ghorbani, Ehsan Sadrossadat, Jafar Bolouri Bazaz, et al.
Geotechnical and Geological Engineering (2018) Vol. 36, Iss. 4, pp. 2057-2076
Closed Access | Times Cited: 45
Comprehensive evaluation of machine learning algorithms applied to TBM performance prediction
Jie Yang, Saffet Yağız, Yingjing Liu, et al.
Underground Space (2021) Vol. 7, Iss. 1, pp. 37-49
Open Access | Times Cited: 29
Prediction of ground subsidence by shield tunneling using ensemble learning
Dukun Zhao, Zhangang Sun, Yueji He, et al.
Tunnelling and Underground Space Technology (2023) Vol. 141, pp. 105343-105343
Closed Access | Times Cited: 13
Artificial intelligence in tunnel construction: A comprehensive review of hotspots and frontier topics
Lianbaichao Liu, Zhanping Song, Xu Li
Geohazard Mechanics (2023) Vol. 2, Iss. 1, pp. 1-12
Open Access | Times Cited: 12
A Case Study on Advanced CPT Data Interpretation: From Stratification to Soil Parameters
Islam Marzouk, Andreas‐Nizar Granitzer, Stefan Rauter, et al.
Geotechnical and Geological Engineering (2024) Vol. 42, Iss. 5, pp. 4087-4113
Open Access | Times Cited: 4
Multi-step real-time prediction of hard-rock TBM penetration rate combining temporal convolutional network and squeeze-and-excitation block
Li Long, Zaobao Liu, Xingli Fang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
High-precision cutterhead torque prediction for tunnel boring machines using an attention-based embedded LSTM neural network
Yi Qin, Jianghong Zhou, Dengyu Xiao, et al.
Measurement (2023) Vol. 224, pp. 113888-113888
Closed Access | Times Cited: 10
Optimal AI Model for Accurate Classification of Squeezing in Underground Structures
Hadi Fattahi, Ehsan Mohtarami
Geotechnical and Geological Engineering (2025) Vol. 43, Iss. 2
Closed Access
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