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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:

ANN-Based Dynamic Prediction of Daily Ground Settlement of Foundation Pit Considering Time-Dependent Influence Factors
Zhenyu Zhang, Rongqiao Xu, Xi Wu, et al.
Applied Sciences (2022) Vol. 12, Iss. 13, pp. 6324-6324
Open Access | Times Cited: 12

Showing 12 citing articles:

Tunnel deformation prediction during construction: An explainable hybrid model considering temporal and static factors
Zhonghao Li, Enlin Ma, Jinxing Lai, et al.
Computers & Structures (2024) Vol. 294, pp. 107276-107276
Closed Access | Times Cited: 20
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
Predicting coefficient of permeability of soils: an interpretable machine learning approach augmented by deep generative adversarial network
Laiba Gulaly, Muhammad Luqman, Hammanjoda Usman, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 2
Closed Access
A Spatiotemporal-Adaptive-Network-Based Method for Predicting Axial Forces in Assembly Steel Struts with Servo System of Foundation Pits
Weiwei Liu, Jianchao Sheng, Jian Zhou, et al.
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2343-2343
Open Access
Application of machine learning in early warning system of geotechnical disaster: a systematic and comprehensive review
Shan Lin, Zenglong Liang, Hongwei Guo, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 6
Open Access
Comprehensive review and future perspectives on prediction and mitigation of tunnel-induced ground settlement: A bibliometric analysis and methodological overview (2002–2022)
Jian Zhou, Hongning Qi, Kang Peng, et al.
Tunnelling and Underground Space Technology (2024) Vol. 154, pp. 106081-106081
Open Access | Times Cited: 3
Unified Description of Viscous Behaviors of Clay and Sand with a Visco-Hypoplastic Model
Shun Wang, Xu Xiao, Xuan Kang, et al.
Springer series in geomechanics and geoengineering (2024), pp. 201-209
Closed Access | Times Cited: 1
Automatic monitoring the risk coupling of foundation pits: integrated point cloud, computer vision and Bayesian networks approach
Xuelai Li, Xincong Yang, Kailun Feng, et al.
Engineering Construction & Architectural Management (2024)
Closed Access | Times Cited: 1
Explainable deep learning-based dynamic prediction of surface settlement considering temporal characteristics during deep excavation
Xuefeng An, Hanbin Luo, Fei Zheng, et al.
Applied Soft Computing (2024), pp. 112273-112273
Closed Access | Times Cited: 1
Transformer based neural network for daily ground settlement prediction of foundation pit considering spatial correlation
Xiaofeng Wu, Yang Song, Di Zhang, et al.
PLoS ONE (2023) Vol. 18, Iss. 11, pp. e0294501-e0294501
Open Access | Times Cited: 3
Land Subsidence Prediction Model Based on the Long Short-Term Memory Neural Network Optimized Using the Sparrow Search Algorithm
Peicheng Qiu, Fei Liu, Jiaming Zhang
Applied Sciences (2023) Vol. 13, Iss. 20, pp. 11156-11156
Open Access | Times Cited: 2
Strength Prediction of Agro Waste Mixed Composites Using a Neural Network Regression Model
H. R. Mahalingegowda, B. K. Narendra
Journal of The Institution of Engineers (India) Series D (2024)
Closed Access
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