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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:

The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate
Jui‐Sheng Chou, Ngoc-Tri Ngo, Wai K. Chong
Engineering Applications of Artificial Intelligence (2016) Vol. 65, pp. 471-483
Closed Access | Times Cited: 106

Showing 26-50 of 106 citing articles:

On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm
Mohamed El Amine Ben Seghier, José A. F. O. Corriea, Jafar Jafari‐Asl, et al.
Neural Computing and Applications (2021) Vol. 33, Iss. 23, pp. 15969-15985
Closed Access | Times Cited: 46
Intelligent Risk Management in Construction Projects: Systematic Literature Review
Liao Chenya, Eeydzah Aminudin, Suzila Mohd, et al.
IEEE Access (2022) Vol. 10, pp. 72936-72954
Open Access | Times Cited: 37
Comparison of machine learning models to provide preliminary forecasts of real estate prices
Jui‐Sheng Chou, Dillon-Brandon Fleshman, Dinh‐Nhat Truong
Journal of Housing and the Built Environment (2022) Vol. 37, Iss. 4, pp. 2079-2114
Closed Access | Times Cited: 29
Deeppipe: Theory-guided prediction method based automatic machine learning for maximum pitting corrosion depth of oil and gas pipeline
Jian Du, Jianqin Zheng, Yongtu Liang, et al.
Chemical Engineering Science (2023) Vol. 278, pp. 118927-118927
Closed Access | Times Cited: 23
Explainable ensemble learning framework for estimating corrosion rate in suspension bridge main cables
Alejandro Jiménez Ríos, Mohamed El Amine Ben Seghier, Vagelis Plevris, et al.
Results in Engineering (2024) Vol. 23, pp. 102723-102723
Open Access | Times Cited: 7
Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa
Senthil Krishnamurthy, Oludamilare Bode Adewuyi, Emmanuel Luwaca, et al.
Energy Conversion and Management X (2024) Vol. 24, pp. 100772-100772
Open Access | Times Cited: 7
Shear strength prediction of reinforced concrete beams by baseline, ensemble, and hybrid machine learning models
Jui‐Sheng Chou, Thi-Phuong-Trang Pham, Thi-Kha Nguyen, et al.
Soft Computing (2019) Vol. 24, Iss. 5, pp. 3393-3411
Closed Access | Times Cited: 51
Hybrid machine learning for predicting strength of sustainable concrete
Anh‐Duc Pham, Ngoc-Tri Ngo, Quang Trung Nguyen, et al.
Soft Computing (2020) Vol. 24, Iss. 19, pp. 14965-14980
Closed Access | Times Cited: 48
Estimation of Concentration Values of Different Gases Based on Long Short-Term Memory by Using Electronic Nose
Hande Bakiler, Selda Güney
Biomedical Signal Processing and Control (2021) Vol. 69, pp. 102908-102908
Closed Access | Times Cited: 37
An Ensemble Machine Learning Model for Enhancing the Prediction Accuracy of Energy Consumption in Buildings
Ngoc-Tri Ngo, Anh‐Duc Pham, Thi Thu Ha Truong, et al.
Arabian Journal for Science and Engineering (2021) Vol. 47, Iss. 4, pp. 4105-4117
Closed Access | Times Cited: 36
A review of currently applied building information modeling tools of constructions in China
Jingming Li, Nianping Li, Jinqing Peng, et al.
Journal of Cleaner Production (2018) Vol. 201, pp. 358-368
Closed Access | Times Cited: 46
Automatic recognition of concrete spall using image processing and metaheuristic optimized LogitBoost classification tree
Minh-Tu Cao, Ngoc‐Mai Nguyen, Kuan-Tsung Chang, et al.
Advances in Engineering Software (2021) Vol. 159, pp. 103031-103031
Closed Access | Times Cited: 30
Application of machine learning in material corrosion research
Shuaijie Ma, Yanxia Du, Shasha Wang, et al.
Corrosion Reviews (2023) Vol. 41, Iss. 4, pp. 417-426
Closed Access | Times Cited: 12
Towards the automated interpretation of impedance spectra from organic coatings using neural networks
Vincenzo Bongiorno, Emmanouela Michailidou, M. Curioni
Corrosion Science (2024) Vol. 230, pp. 111922-111922
Closed Access | Times Cited: 4
Prediction Model for Pipeline Pitting Corrosion Based on Multiple Feature Selection and Residual Correction
Zhenhao Zhu, Qiushuang Zheng, Hongbing Liu, et al.
Journal of Marine Science and Application (2024)
Closed Access | Times Cited: 4
Analysis of training parameters in the ANN learning process to mapping the concrete carbonation depth
Emerson Felipe Félix, Edna Possan, Rogério Carrazedo
Journal of Building Pathology and Rehabilitation (2019) Vol. 4, Iss. 1
Closed Access | Times Cited: 35
Engineering strength of fiber-reinforced soil estimated by swarm intelligence optimized regression system
Jui‐Sheng Chou, Ngoc-Tri Ngo
Neural Computing and Applications (2016) Vol. 30, Iss. 7, pp. 2129-2144
Closed Access | Times Cited: 34
Predicting the Parabolic Rate Constants of High-Temperature Oxidation of Ti Alloys Using Machine Learning
Somesh Kr. Bhattacharya, Ryoji Sahara, Takayuki Narushima
Oxidation of Metals (2020) Vol. 94, Iss. 3-4, pp. 205-218
Closed Access | Times Cited: 28
Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review
Tan Shin Yee, Nawaf H. M. M. Shrifan, Ahmed Jamal Abdullah Al-Gburi, et al.
IEEE Access (2022) Vol. 10, pp. 88191-88210
Open Access | Times Cited: 17
State-of-the-art progress on artificial intelligence and machine learning in accessing molecular coordination and adsorption of corrosion inhibitors
Taiwo W. Quadri, Ekemini D. Akpan, Saheed E. Elugoke, et al.
Applied Physics Reviews (2025) Vol. 12, Iss. 1
Closed Access
Erosion-Corrosion: An Up-To-Date Review of Origin, Impacts, Affecting Factors, Occurrence Areas, Experimental Measuring Devices, and Research Advances
T. N. Guma, DD Ishaya, EA Abhulimen, et al.
International Journal of Latest Technology in Engineering Management & Applied Science (2025) Vol. 13, Iss. 12, pp. 136-171
Closed Access
Time-series prediction of steel corrosion in concrete using recurrent neural networks
Haodong Ji, Jin-Cheng Liu, Hailong Ye
Nondestructive Testing And Evaluation (2025), pp. 1-20
Closed Access
Predicting Mechanical Properties of Concrete Structures Using Metaheuristic-Optimization-Based Machine Learning Models
Ngoc‐Mai Nguyen
Applied Soft Computing (2025), pp. 112893-112893
Closed Access
EVALUATION OF THE EFFECTS OF CORROSION OF CARBON STEEL IN ACID MINE DRAINAGES BY USING AN EXPLAINABLE ARTIFICIAL INTELLIGENCE MODEL.
M. L. de la Torre, J. Aroba, José Miguel Dávila Martín, et al.
Results in Engineering (2025), pp. 104380-104380
Open Access
Prediction and Detection of Localised Corrosion Attack of Stainless Steel in Biogas Production: A Machine Learning Classification Approach
M.J. Jiménez–Come, Francisco Javier González Gallero, Pascual Álvarez Gómez, et al.
Materials (2025) Vol. 18, Iss. 5, pp. 1057-1057
Open Access

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