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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:

Ensemble machine learning model for corrosion initiation time estimation of embedded steel reinforced self-compacting concrete
Babatunde Abiodun Salami, Syed Masiur Rahman, Tajudeen A. Oyehan, et al.
Measurement (2020) Vol. 165, pp. 108141-108141
Closed Access | Times Cited: 87

Showing 26-50 of 87 citing articles:

Prognosis of compressive strength of fly‐ash‐based geopolymer‐modified sustainable concrete with ML algorithms
Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor, et al.
Structural Concrete (2022) Vol. 24, Iss. 3, pp. 3990-4014
Closed Access | Times Cited: 28
Early-age compressive strength prediction of cemented phosphogypsum backfill using lab experiments and ensemble learning models
Chendi Min, Xiong Shuai, Ying Shi, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02107-e02107
Open Access | Times Cited: 16
Gaussian Mixture Model-Based Classification of Corrosion Severity in Concrete Structures Using Ultrasonic Imaging
Prasanna Kumar Mayakuntla, Abhijit Ganguli, Danny Smyl
Journal of Nondestructive Evaluation (2023) Vol. 42, Iss. 2
Open Access | Times Cited: 15
Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis
Leonardo Bertolucci Coelho, Daniel Torres, Vincent Vangrunderbeek, et al.
npj Materials Degradation (2023) Vol. 7, Iss. 1
Open Access | Times Cited: 15
Progress in Artificial Intelligence-based Prediction of Concrete Performance
Xiangxin Hu, Bixiong Li, Yelan Mo, et al.
Journal of Advanced Concrete Technology (2021) Vol. 19, Iss. 8, pp. 924-936
Open Access | Times Cited: 32
Evaluation of interlayer bonding in layered composites based on non-destructive measurements and machine learning: Comparative analysis of selected learning algorithms
Sławomir Czarnecki, Łukasz Sadowski, Jerzy Hoła
Automation in Construction (2021) Vol. 132, pp. 103977-103977
Open Access | Times Cited: 28
Comparative study of corrosion-based service life prediction of reinforced concrete structures using traditional and machine learning approach
Amgoth Rajender, Amiya K. Samanta, Animesh Paral
International Journal of Structural Integrity (2024)
Closed Access | Times Cited: 4
Objective characterisation of reinforced concrete with progressive corrosion defects through clustering and thresholding of infrared images
Masoud Pedram, Susan Taylor, G. A. Hamill, et al.
Measurement (2023) Vol. 225, pp. 114017-114017
Open Access | Times Cited: 11
Machine Learning-Aided Prediction of Seismic Response of RC Bridge Piers Exposed to Chloride-Induced Corrosion
Pooria Poorahad Anzabi, M.R. Shiravand, S. Mahboubi
Lecture notes in civil engineering (2025), pp. 1409-1421
Closed Access
AI-Powered Optimization of Engineered Cementitious Composites Properties and CO₂ Emissions for Sustainable Construction
Qiuying Chang, Chuanhai Zhao, Ali H. AlAteah, et al.
Case Studies in Construction Materials (2025), pp. e04405-e04405
Open Access
Prediction of Corrosion Initiation Time in Sustainable Concrete Blended with Microsilica and Portland Slag Cement Under Extreme Chloride Conditions
Amgoth Rajender, Amiya K. Samanta
Arabian Journal for Science and Engineering (2025)
Closed Access
AI-based framework for concrete durability assessment using generative adversarial networks and bayesian neural networks
Abobaker Ba Ragaa, Fahim Al-Neshawy, Mohamed Noureldin
Construction and Building Materials (2025) Vol. 471, pp. 140722-140722
Open Access
An intelligent approach for predicting strength of roadbed foam lightweight concrete based on optimized XGBoost model
Wanmao Zhang, Dunwen Liu, Yu Tang, et al.
Case Studies in Construction Materials (2025), pp. e04702-e04702
Open Access
Machine learning in concrete durability: challenges and pathways identified by RILEM TC 315-DCS towards enhanced predictive models
Woubishet Zewdu Taffese, Benoît Hilloulin, Yury Villagrán Zaccardi, et al.
Materials and Structures (2025) Vol. 58, Iss. 4
Open Access
A state-of-the-art review on monitoring technology and characterization of reinforcement corrosion in concrete
Qiang Li, Jinsong Lan, Luming Shen, et al.
Case Studies in Construction Materials (2025), pp. e04780-e04780
Open Access
Deep learning and machine learning‐based prediction of capillary water absorption of hybrid fiber reinforced self‐compacting concrete
Ceren Kına, Kâzım Türk, Harun Tanyıldızı
Structural Concrete (2022) Vol. 23, Iss. 5, pp. 3331-3358
Closed Access | Times Cited: 15
Performance Prediction of Cement Stabilized Soil Incorporating Solid Waste and Propylene Fiber
Genbao Zhang, Zhiqing Ding, Yufei Wang, et al.
Materials (2022) Vol. 15, Iss. 12, pp. 4250-4250
Open Access | Times Cited: 15
Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review
Asraar Anjum, Meftah Hrairi, Abdul Aabid, et al.
Frattura ed Integrità Strutturale (2024) Vol. 18, Iss. 69, pp. 43-59
Open Access | Times Cited: 3
Language model enhanced surface chloride concentration determination for concrete within splash environment based on limited field records
Xinrui Ma, Xiao Liang, Shuai Wang, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03157-e03157
Open Access | Times Cited: 3
Neuro-swarm and neuro-imperialism techniques to investigate the compressive strength of concrete constructed by freshwater and magnetic salty water
Mohammad Khorshidi Paji, Behrouz Gordan, Morteza Biklaryan, et al.
Measurement (2021) Vol. 182, pp. 109720-109720
Closed Access | Times Cited: 19
Early predicting tribocorrosion rate of dental implant titanium materials using random forest machine learning models
Remya Ampadi Ramachandran, Valentim Adelino Ricardo Barão, Didem Ozevin, et al.
Tribology International (2023) Vol. 187, pp. 108735-108735
Open Access | Times Cited: 8
Automated measurement system for detecting carbonation depth: Image-processing based technique applied to concrete sprayed with phenolphthalein
Nicola Giulietti, Paolo Chiariotti, Gloria Cosoli, et al.
Measurement (2021) Vol. 175, pp. 109142-109142
Closed Access | Times Cited: 18
Intelligent modeling of dye removal by aluminized activated carbon
Saheed Adewale Lateef, Ismaila A. Oyehan, Tajudeen A. Oyehan, et al.
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 39, pp. 58950-58962
Closed Access | Times Cited: 12
Prediction of concrete properties using machine learning algorithm
Rupesh Kumar Tipu, V. R. Panchal, Kartik S. Pandya
Journal of Physics Conference Series (2022) Vol. 2273, Iss. 1, pp. 012016-012016
Open Access | Times Cited: 12
Using explainable machine learning to predict compressive strength of blended concrete: a data-driven metaheuristic approach
Mohammad Tamim Kashifi, Babatunde Abiodun Salami, Syed Masiur Rahman, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 219-236
Closed Access | Times Cited: 7

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