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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:

A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger
Gang Shang, Liyun Xu, Jinzhu Tian, et al.
Energy (2023) Vol. 274, pp. 127326-127326
Closed Access | Times Cited: 17

Showing 17 citing articles:

A deep learning method for the prediction of ship fuel consumption in real operational conditions
Mingyang Zhang, Nikolaos Tsoulakos, Pentti Kujala, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 130, pp. 107425-107425
Open Access | Times Cited: 57
Existing technologies and scientific advancements to decarbonize shipping by retrofitting
Aleksander A. Kondratenko, Mingyang Zhang, Sasan Tavakoli, et al.
Renewable and Sustainable Energy Reviews (2025) Vol. 212, pp. 115430-115430
Open Access | Times Cited: 2
Digital-twin-based predictive compensation control strategy for seam tracking in steel sheets welding of large cruise ships
Gang Shang, Liyun Xu, Zufa Li, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 88, pp. 102725-102725
Closed Access | Times Cited: 11
Investigation of ship energy consumption based on neural network
Yaqing Shu, Benshuang yu, Wei Liu, et al.
Ocean & Coastal Management (2024) Vol. 254, pp. 107167-107167
Closed Access | Times Cited: 6
A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method
Tian Lan, Lianzhong Huang, Ranqi Ma, et al.
Ocean Engineering (2024) Vol. 307, pp. 118156-118156
Closed Access | Times Cited: 6
Prediction of seam tracking errors in the intelligent welding system: A rapid prediction method based on real-time monitoring data
Gang Shang, Liyun Xu, Zufa Li, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103124-103124
Closed Access
Data-driven multi-objective prediction and optimization of construction productivity and energy consumption in cutter suction dredging
Yong Chen, Qiubing Ren, Mingchao Li, et al.
Automation in Construction (2025) Vol. 173, pp. 106104-106104
Closed Access
Optimization of ethanol and methanol blends for enhanced performance and reduced emissions in BS-VI compliant motorbike engines
Ratchagaraja Dhairiyasamy, Deepika Gabiriel, Wasurat Bunpheng, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 2
Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer
Zhecheng Long, Shidong Fan, Qian Gao, et al.
Applied Sciences (2024) Vol. 14, Iss. 10, pp. 4186-4186
Open Access | Times Cited: 1
A robust method of dual adaptive prediction for ship fuel consumption based on polymorphic particle swarm algorithm driven
Tian Lan, Lianzhong Huang, Ranqi Ma, et al.
Applied Energy (2024) Vol. 379, pp. 124911-124911
Closed Access | Times Cited: 1
Thermal conductivity of porous building materials: An exploration of new challenges in fractal modelling solutions
Giorgio Pia, Marta Cappai
RILEM Technical Letters (2023) Vol. 8, pp. 79-93
Open Access | Times Cited: 3
Investigation of Ship Energy Consumption Based on Neural Network
Yaqing Shu, Benshuang yu, Wei Liu, et al.
(2024)
Closed Access
Data-Driven Method for Vacuum Prediction in the Underwater Pump of a Cutter Suction Dredger
Hualin Chen, Zihao Yuan, Wangming Wang, et al.
Processes (2024) Vol. 12, Iss. 4, pp. 812-812
Open Access
Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search
Hao Liu, Ruizhe Liu, Shuo Bai, et al.
Water (2024) Vol. 16, Iss. 21, pp. 3067-3067
Open Access
Expanding the range of ship fuel consumption prediction: A multi-algorithm feature selection approach
Shengyu Piao, Min-Ho Park, Siljung Yeo, et al.
Ocean Engineering (2024) Vol. 316, pp. 119944-119944
Closed Access
Warehouse equipment maintenance strategy based on the prediction of total amount of stock in & out operations
Gang Shang, Liyun Xu, Chunqiang Lu, et al.
Procedia CIRP (2023) Vol. 120, pp. 237-242
Open Access | Times Cited: 1
Prediction of the Reaming Torque Using Artificial Neural Network and Random Forest Algorithm: Comparative Performance Analysis
M. C. Rakshith, Raghavendra C. Kamath, G S Vijay
(2023), pp. 97-97
Open Access
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