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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:

Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity
Navaratnarajah Sathiparan, Pratheeba Jeyananthan, Daniel Niruban Subramaniam
Measurement (2023) Vol. 225, pp. 114006-114006
Closed Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

Investigation of impact of aggregate shape on pervious concrete using machine learning classification methods
Sathushka Heshan Bandara Wijekoon, Navakulan Ahilash, Varatharaja Pravinjan, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 143, pp. 110008-110008
Closed Access | Times Cited: 2
A machine learning approach to predicting pervious concrete properties: a review
Navaratnarajah Sathiparan, Pratheeba Jeyananthan, Daniel Niruban Subramaniam
Innovative Infrastructure Solutions (2025) Vol. 10, Iss. 2
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
AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements
Mouhcine Benaicha
Sustainability (2024) Vol. 16, Iss. 15, pp. 6644-6644
Open Access | Times Cited: 5
Predictive modeling of compressive strength in glass powder blended pervious concrete
Navaratnarajah Sathiparan, Daniel Niruban Subramaniam
Asian Journal of Civil Engineering (2025)
Closed Access
Advanced Nondestructive Monitoring and Detection Apparatus against Marine Concrete Durability: A Review
Xiangbo Xu, Mingzhe Zhang, Zhe Li, et al.
Journal of structural design and construction practice. (2025) Vol. 30, Iss. 2
Closed Access
Predicting the strength of alkali-activated masonry blocks using machine learning models: geopolymer mortar with quarry waste, rice husk ash, and eggshell ash
Anis Ahamed, S. Sakeek Yamani, L. S. Dissanayaka, et al.
Journal of Building Pathology and Rehabilitation (2025) Vol. 10, Iss. 1
Closed Access
Predicting the performance of pervious concrete pavements using artificial intelligence
Abdulkader El-Mir, Dana Nasr, Hilal El-Hassan
Elsevier eBooks (2025), pp. 319-343
Closed Access
Permeability measurement and prediction of pervious concrete pavements
Ricardo Pieralisi, Fábio Cunha Lofrano, Rafael Jansen Mikami, et al.
Elsevier eBooks (2025), pp. 271-298
Closed Access
Prediction of characteristics of pervious concrete by machine learning technique using mix parameters and non-destructive test measurements
Navaratnarajah Sathiparan, Sathushka Heshan Bandara Wijekoon, Pratheeba Jeyananthan, et al.
Nondestructive Testing And Evaluation (2025), pp. 1-50
Closed Access
BETON DAYANIMI TAHMİNİNDE İKİLİ VE ÇOKLU DOĞRUSAL REGRESYON ANALİZLERİNİN KARŞILAŞTIRILMASI
Nevbahar Ekin
Mühendislik Bilimleri ve Tasarım Dergisi (2025) Vol. 13, Iss. 1, pp. 64-77
Open Access
Efeito do tipo de cimento e de materiais cimentícios suplementares na resistividade elétrica superficial do concreto
Marcelo Henrique Farias de Medeiros, Rayane Campos Lopes, L. V. Real, et al.
Ambiente Construído (2025) Vol. 25
Open Access
Compressive strength prediction of sleeve grouting materials in prefabricated structures using hybrid optimized XGBoost models
Yanqi Wu, D. Cai, Sheng Gu, et al.
Construction and Building Materials (2025) Vol. 476, pp. 141319-141319
Closed Access
Acid effect on permeable polymer concrete containing different resin and aggregate types
A. Özdemir, Serdal Ünal, Arda Büyüksungur, et al.
Journal of Building Engineering (2025) Vol. 106, pp. 112668-112668
Closed Access
Predictive Modelling of Mechanical Properties of Concrete Using Machine Learning with Secondary Treated Waste Water and Fly Ash
Kumar Rajiv, Y Ramalinga Reddy, G Shiva Kumar, et al.
Cleaner Waste Systems (2025), pp. 100296-100296
Open Access
The Prediction of Pervious Concrete Compressive Strength Based on a Convolutional Neural Network
Gaoming Yu, Senlai Zhu, Ziru Xiang
Buildings (2024) Vol. 14, Iss. 4, pp. 907-907
Open Access | Times Cited: 3
Characterization of porosities and optimization of mix design of pervious concrete using image analysis
Jeyaseelan Shobijan, Mathuranayagam Arunan, Sivaranjan Pratheesh, et al.
Journal of Sustainable Cement-Based Materials (2024) Vol. 13, Iss. 8, pp. 1149-1163
Closed Access | Times Cited: 3
Determination of concrete compressive strength from surface images with the integration of CNN and SVR methods
Gaffari Çelik, Muhammet Ozdemir
Measurement (2024) Vol. 238, pp. 115331-115331
Closed Access | Times Cited: 3
Fast concrete crack depth detection using low frequency ultrasound array SH waves data
Jian Shen, Liu Liu, Zhenming Shi, et al.
Journal of Applied Geophysics (2024), pp. 105530-105530
Closed Access | Times Cited: 3
Response surface regression and machine learning models to predict the porosity and compressive strength of pervious concrete based on mix design parameters
Navaratnarajah Sathiparan, Sathushka Heshan Bandara Wijekoon, Rinduja Ravi, et al.
Road Materials and Pavement Design (2024), pp. 1-40
Closed Access | Times Cited: 2
Performance Analysis Relevant to Primary Design Parameters of Pervious Concrete: A Critical Review
Daniel Niruban Subramaniam, Arulanantham Anburuvel
Transportation Research Record Journal of the Transportation Research Board (2024)
Closed Access | Times Cited: 2
Image analysis as a geometry- and integrity-independent tool for predicting strength of cemented tailings backfill using slag-based binder
Sunqiang Yu, Haiqiang Jiang, Zhangyao Xi, et al.
Construction and Building Materials (2024) Vol. 444, pp. 137867-137867
Closed Access | Times Cited: 2
Investigation and prediction of impact of aggregate size and shape on porosity and compressive strength of pervious concrete
Navakulan Ahilash, Jeyaseelan Shobijan, Mathuranayagam Arunan, et al.
International Journal of Pavement Engineering (2024) Vol. 25, Iss. 1
Closed Access | Times Cited: 2
Effect of soil characteristics on compressive strength of cement stabilized earth blocks: Statistical analysis and Artificial Neural Networks model
Navaratnarajah Sathiparan, Pratheeba Jeyananthan
Research Square (Research Square) (2024)
Open Access | Times Cited: 1
Statistical investigation of aggregate size and shape impact on porosity and compressive strength of pervious concrete
M. Sajeevan, Navakulan Ahilash, Jeyaseelan Shobijan, et al.
Road Materials and Pavement Design (2024), pp. 1-30
Closed Access | Times Cited: 1
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