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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:

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

Showing 1-25 of 173 citing articles:

BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives
Mengqi Huang, Jelena Ninić, Qianbing Zhang
Tunnelling and Underground Space Technology (2020) Vol. 108, pp. 103677-103677
Open Access | Times Cited: 266
Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate
Jian Zhou, Yingui Qiu, Shuangli Zhu, et al.
Engineering Applications of Artificial Intelligence (2020) Vol. 97, pp. 104015-104015
Closed Access | Times Cited: 259
Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques
Jian Zhou, Yingui Qiu, Danial Jahed Armaghani, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101091-101091
Open Access | Times Cited: 229
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: 183
From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0
Riccardo Rosati, Luca Romeo, Gianalberto Cecchini, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 1, pp. 107-121
Open Access | Times Cited: 108
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
Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study
Junwei Ma, Ding Xia, Haixiang Guo, et al.
Landslides (2022) Vol. 19, Iss. 10, pp. 2489-2511
Open Access | Times Cited: 90
Real-time hard-rock tunnel prediction model for rock mass classification using CatBoost integrated with Sequential Model-Based Optimization
Yin Bo, Quansheng Liu, Xing Huang, et al.
Tunnelling and Underground Space Technology (2022) Vol. 124, pp. 104448-104448
Closed Access | Times Cited: 81
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
Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions
Xianguo Wu, Xinyi Li, Yawei Qin, et al.
Applied Energy (2023) Vol. 339, pp. 120934-120934
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: 42
Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning
Xianguo Wu, Zongbao Feng, Jun Liu, et al.
Automation in Construction (2024) Vol. 165, pp. 105516-105516
Closed Access | Times Cited: 26
Prediction of surface settlement around subway foundation pits based on spatiotemporal characteristics and deep learning models
Wensong Zhang, Ying Yuan, Meng Long, et al.
Computers and Geotechnics (2024) Vol. 168, pp. 106149-106149
Closed Access | Times Cited: 18
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
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
Comparison of machine learning methods for ground settlement prediction with different tunneling datasets
Libin Tang, SeonHong Na
Journal of Rock Mechanics and Geotechnical Engineering (2021) Vol. 13, Iss. 6, pp. 1274-1289
Open Access | Times Cited: 101
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
Tunnel boring machine vibration-based deep learning for the ground identification of working faces
Mengbo Liu, Shaoming Liao, Yifeng Yang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2021) Vol. 13, Iss. 6, pp. 1340-1357
Open Access | Times Cited: 71
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
A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting
Yong Dai, Manoj Khandelwal, Yingui Qiu, et al.
Neural Computing and Applications (2022) Vol. 34, Iss. 8, pp. 6273-6288
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
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
Machine Learning-Based Modelling of Soil Properties for Geotechnical Design: Review, Tool Development and Comparison
Pin Zhang, Zhen‐Yu Yin, Yin-Fu Jin
Archives of Computational Methods in Engineering (2021) Vol. 29, Iss. 2, pp. 1229-1245
Closed Access | Times Cited: 61
Random forest-based predictors for driving forces of earth pressure balance (EPB) shield tunnel boring machine (TBM)
Xiangxun Kong, Xianzhang Ling, Liang Tang, et al.
Tunnelling and Underground Space Technology (2022) Vol. 122, pp. 104373-104373
Closed Access | Times Cited: 54
Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement
Dongmei Zhang, Yiming Shen, Zhongkai Huang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 14, Iss. 4, pp. 1100-1114
Open Access | Times Cited: 50
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