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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:

Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface
W.K.V.J.B. Kulasooriya, R.S.S. Ranasinghe, Udara Sachinthana Perera, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 34

Showing 1-25 of 34 citing articles:

Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms
Mana Alyami, Majid Khan, Muhammad Fawad, et al.
Case Studies in Construction Materials (2023) Vol. 20, pp. e02728-e02728
Open Access | Times Cited: 55
Adapting cities to the surge: A comprehensive review of climate-induced urban flooding
Gangani Dharmarathne, Anushka Osadhi Waduge, Madhusha Bogahawaththa, et al.
Results in Engineering (2024) Vol. 22, pp. 102123-102123
Open Access | Times Cited: 53
A novel machine learning approach for diagnosing diabetes with a self-explainable interface
Gangani Dharmarathne, Thilini N. Jayasinghe, Madhusha Bogahawaththa, et al.
Healthcare Analytics (2024) Vol. 5, pp. 100301-100301
Open Access | Times Cited: 35
Predicting transient wind loads on tall buildings in three-dimensional spatial coordinates using machine learning
D.P.P. Meddage, Damith Mohotti, Kasun Wijesooriya
Journal of Building Engineering (2024) Vol. 85, pp. 108725-108725
Open Access | Times Cited: 29
Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques
Charuni I. Madhushani, K. G. S. Dananjaya, I.U. Ekanayake, et al.
Journal of Hydrology (2024) Vol. 631, pp. 130846-130846
Closed Access | Times Cited: 24
Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning
R.S.S. Ranasinghe, W.K.V.J.B. Kulasooriya, Udara Sachinthana Perera, et al.
Results in Engineering (2024) Vol. 23, pp. 102503-102503
Open Access | Times Cited: 22
An explainable machine learning approach to predict the compressive strength of graphene oxide-based concrete
D.P.P. Meddage, Isuri Fonseka, Damith Mohotti, et al.
Construction and Building Materials (2024) Vol. 449, pp. 138346-138346
Open Access | Times Cited: 21
A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI
U.A.K.K. Perera, D.T.S. Coralage, I.U. Ekanayake, et al.
Results in Engineering (2024) Vol. 21, pp. 101920-101920
Open Access | Times Cited: 19
Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models
Irfan Ullah, Muhammad Faisal Javed, Hisham Alabduljabbar, et al.
Structures (2025) Vol. 71, pp. 108138-108138
Closed Access | Times Cited: 2
A novel explainable AI-based approach to estimate the natural period of vibration of masonry infill reinforced concrete frame structures using different machine learning techniques
P. Thisovithan, Harinda Aththanayake, D.P.P. Meddage, et al.
Results in Engineering (2023) Vol. 19, pp. 101388-101388
Open Access | Times Cited: 42
On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence
Gangani Dharmarathne, Madhusha Bogahawaththa, Marion McAfee, et al.
Intelligent Systems with Applications (2024) Vol. 22, pp. 200397-200397
Open Access | Times Cited: 10
Ensemble learning based compressive strength prediction of concrete structures through real-time non-destructive testing
Harish Chandra Arora, Bharat Bhushan, Aman Kumar, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
The disagreement dilemma in explainable AI: can bias reduction bridge the gap
Nitanshi Bhardwaj, Gaurav Parashar
International Journal of Systems Assurance Engineering and Management (2025)
Closed Access | Times Cited: 1
Machine Learning as an Innovative Engineering Tool for Controlling Concrete Performance: A Comprehensive Review
Fatemeh Mobasheri, Masoud Hosseinpoor, Ammar Yahia, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access | Times Cited: 1
Explainable artificial intelligence framework for FRP composites design
Mostafa Yossef, Mohamed Noureldin, Aghyad Alqabbany
Composite Structures (2024) Vol. 341, pp. 118190-118190
Open Access | Times Cited: 6
Machine learning approach to predict the mechanical properties of cementitious materials containing carbon nanotubes
Nader M. Okasha, Masoomeh Mirrashid, Hosein Naderpour, et al.
Developments in the Built Environment (2024) Vol. 19, pp. 100494-100494
Open Access | Times Cited: 5
Predicting compressive strength of ultra-high-performance concrete using Naive Bayes regression in novel approaches
Zheng Zhao
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5235-5249
Closed Access | Times Cited: 4
Recent Applications of Explainable AI (XAI): A Systematic Literature Review
Mirka Saarela, Vili Podgorelec
Applied Sciences (2024) Vol. 14, Iss. 19, pp. 8884-8884
Open Access | Times Cited: 4
Prediction of California Bearing Ratio of nano-silica and bio-char stabilized soft sub-grade soils using explainable machine learning
Ishwor Thapa, Sufyan Ghani, Abdul Waris Kenue, et al.
Transportation Geotechnics (2024), pp. 101387-101387
Closed Access | Times Cited: 4
Application of optimized random forest simulation on flexural strength of basalt fiber reinforced concrete
Hongliang Yue, Yan Wang, Liang Xiao-yong, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2025) Vol. 8, Iss. 4
Closed Access
Using synthetic data to develop machine learning models to predict the performance of fiber-reinforced concrete
Victor Hugo Peres Silva, Carolina Luiza Emereciana Pessoa, Derica dos Santos Sousa, et al.
Asian Journal of Civil Engineering (2025)
Closed Access
Explainable machine learning model for predicting compressive strength of CO2-cured concrete
Jia Chu, Bingbing Guo, Taotao Zhong, et al.
Case Studies in Construction Materials (2025), pp. e04589-e04589
Open Access
Mechanism of KH550 Improving Basalt Fiber Cement-Based Interface and Mechanical Properties
Yue Liu, Le Yu, Zi Lin Wang
Applied Mechanics and Materials (2025) Vol. 925, pp. 91-103
Closed Access
Explainable Artificial Intelligence (XAI) to interpret the contributing factors of land subsidence susceptibility prediction model
Siti Noratiqah Mohamad Deros, Mohamad Razin Naim, Norashidah Md Din
Total Environment Advances (2025), pp. 200129-200129
Closed Access
Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies
Fangyuan Li, Md. Sohel Rana, Muhammad Ahmed Qurashi
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 3
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