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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 surface chloride concentration of marine concrete using ensemble machine learning
Rong Cai, Taihao Han, Wenyu Liao, et al.
Cement and Concrete Research (2020) Vol. 136, pp. 106164-106164
Open Access | Times Cited: 210

Showing 26-50 of 210 citing articles:

Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter
Abidhan Bardhan, Navid Kardani, Abdel Kareem Alzo’ubi, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2022) Vol. 14, Iss. 5, pp. 1588-1608
Open Access | Times Cited: 47
Machine learning techniques applied to construction: A hybrid bibliometric analysis of advances and future directions
José García, Gabriel Villavicencio, Francisco Altimiras, et al.
Automation in Construction (2022) Vol. 142, pp. 104532-104532
Closed Access | Times Cited: 47
Projections of corrosion and deterioration of infrastructure in United States coasts under a changing climate
Yating Zhang, Bilal M. Ayyub, Juan F. Fung
Resilient Cities and Structures (2022) Vol. 1, Iss. 1, pp. 98-109
Open Access | Times Cited: 44
Machine learning prediction models for compressive strength of calcined sludge-cement composites
Jinrui Zhang, Wen‐Jun Niu, Youzhi Yang, et al.
Construction and Building Materials (2022) Vol. 346, pp. 128442-128442
Closed Access | Times Cited: 44
Concrete compressive strength prediction using an explainable boosting machine model
Gaoyang Liu, Bochao Sun
Case Studies in Construction Materials (2023) Vol. 18, pp. e01845-e01845
Open Access | Times Cited: 43
Prediction of concrete porosity using machine learning
Chong Cao
Results in Engineering (2022) Vol. 17, pp. 100794-100794
Open Access | Times Cited: 40
Predicting the slump of industrially produced concrete using machine learning: A multiclass classification approach
Xueqing Zhang, Muhammad Zeshan Akber, Wei Zheng
Journal of Building Engineering (2022) Vol. 58, pp. 104997-104997
Closed Access | Times Cited: 39
Modelling the interface bond strength of corroded reinforced concrete using hybrid machine learning algorithms
Tao Huang, Tingbin Liu, Yan Ai, et al.
Journal of Building Engineering (2023) Vol. 74, pp. 106862-106862
Closed Access | Times Cited: 38
Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization
Dawei Yang, Ping Xu, Athar Zaman, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 7198-7218
Open Access | Times Cited: 31
A hybrid strategy of AutoML and SHAP for automated and explainable concrete strength prediction
Bochao Sun, Wenjun Cui, Gaoyang Liu, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02405-e02405
Open Access | Times Cited: 31
Ensemble XGBoost schemes for improved compressive strength prediction of UHPC
May Huu Nguyen, Thuy‐Anh Nguyen, Haï-Bang Ly
Structures (2023) Vol. 57, pp. 105062-105062
Closed Access | Times Cited: 31
Predicting carbonation depth of concrete using a hybrid ensemble model
Zehui Huo, Ling Wang, Yimiao Huang
Journal of Building Engineering (2023) Vol. 76, pp. 107320-107320
Closed Access | Times Cited: 30
New-generation machine learning models as prediction tools for modeling interfacial tension of hydrogen-brine system
Afeez Gbadamosi, Haruna Adamu, Jamilu Usman, et al.
International Journal of Hydrogen Energy (2023) Vol. 50, pp. 1326-1337
Closed Access | Times Cited: 29
Predicting ultra high-performance concrete self-healing performance using hybrid models based on metaheuristic optimization techniques
Bin Xi, Zhewen Huang, Salam Al-Obaidi, et al.
Construction and Building Materials (2023) Vol. 381, pp. 131261-131261
Open Access | Times Cited: 27
Deep learning to predict the hydration and performance of fly ash-containing cementitious binders
Taihao Han, Rohan Bhat, Sai Akshay Ponduru, et al.
Cement and Concrete Research (2023) Vol. 165, pp. 107093-107093
Open Access | Times Cited: 26
Machine learning in coastal bridge hydrodynamics: A state-of-the-art review
Guoji Xu, Chengjie Ji, Yong Xu, et al.
Applied Ocean Research (2023) Vol. 134, pp. 103511-103511
Closed Access | Times Cited: 24
Durability of concrete incorporating recycled coarse aggregates: carbonation and service life prediction under chloride-induced corrosion
Tiago Luis Possebon Ortolan, Pietra Moraes Borges, Laura Silvestro, et al.
Construction and Building Materials (2023) Vol. 404, pp. 133267-133267
Closed Access | Times Cited: 24
Frost resistance of recycled aggregate concrete: A critical review
Kaihua Liu, Kangshen Fu, Yuan Sang, et al.
Journal of Building Engineering (2024) Vol. 90, pp. 109450-109450
Closed Access | Times Cited: 15
Prediction of properties of recycled aggregate concrete using machine learning models: A critical review
Zengfeng Zhao, Yajie Liu, Yanyun Lu, et al.
Journal of Building Engineering (2024) Vol. 90, pp. 109516-109516
Closed Access | Times Cited: 15
Bond properties of GFRP rebars in UHPC under different types of test
Xinyu Hu, Wenyuan Xue, Weichen Xue
Engineering Structures (2024) Vol. 314, pp. 118319-118319
Closed Access | Times Cited: 15
Simulation-free reliability analysis with importance sampling-based adaptive training physics-informed neural networks: Method and application to chloride penetration
Chaolin Song, Rucheng Xiao, Chi Zhang, et al.
Reliability Engineering & System Safety (2024) Vol. 246, pp. 110083-110083
Closed Access | Times Cited: 13
Salt crystallization in porous materials: A quasi-local transport model for evaluating pore filling process
Qing-xiang Xiong, Liang‐yu Tong, Junkai Huang, et al.
Construction and Building Materials (2025) Vol. 462, pp. 139904-139904
Closed Access | Times Cited: 1
An enhancement method for chloride diffusion coefficient long-term prediction based on Hilbert dynamic probabilistic interpolation and BO-LSTM
Renjie Wu, Yuzhou Wang, Khant Swe Hein, et al.
Measurement (2025), pp. 116820-116820
Closed Access | Times Cited: 1
Long-term prediction of surface chloride content of marine concrete structures using inverse physics-informed neural networks
Renjie Wu, Yong Xia, Jin Xia
Engineering Structures (2025) Vol. 329, pp. 119752-119752
Closed Access | Times Cited: 1
A review on properties and multi-objective performance predictions of concrete based on machine learning models
Bowen Ni, Md Zillur Rahman, Shuaicheng Guo, et al.
Materials Today Communications (2025), pp. 112017-112017
Closed Access | Times Cited: 1

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