Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete
Dilshad Kakasor Ismael Jaf, Aso A. Abdalla, Ahmed Salih Mohammed, et al.
Heliyon (2024) Vol. 10, Iss. 4, pp. e25997-e25997
Open Access | Times Cited: 22

Showing 22 citing articles:

Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
Torkan Shafighfard, Farzin Kazemi, Neda Asgarkhani, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 136, pp. 109053-109053
Closed Access | Times Cited: 52
Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review
Rakesh Kumar, Baboo Rai, Pijush Samui
Structural Concrete (2024)
Closed Access | Times Cited: 16
Solid-liquid particle flow sealing mucus (PFSM) in enhancing coalbed methane (CBM) recovery: Multiple-perspectives analysis and mechanism insights
Jianwei Cheng, Dezhi Ran, Qiang Fu, et al.
Construction and Building Materials (2024) Vol. 435, pp. 136670-136670
Closed Access | Times Cited: 9
Experimental study on freeze-thaw failure of concrete incorporating waste tire crumb rubber and analytical evaluation of frost resistance
Xiaoyan Han, Nan Ding, Aijiu Chen, et al.
Construction and Building Materials (2024) Vol. 439, pp. 137356-137356
Closed Access | Times Cited: 8
Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of Engineered Cementitious Composites (ECC)
Waleed Bin Inqiad, Muhammad Faisal Javed, Muhammad Shahid Siddique, et al.
Materials Today Communications (2024) Vol. 39, pp. 109222-109222
Closed Access | Times Cited: 7
A comprehensive review of drop weight impact testing: Evaluating the Pros and Cons in fiber-reinforced concrete performance assessment
G. Murali, Leong Sing Wong, Sallal R. Abid
Journal of Building Engineering (2024) Vol. 94, pp. 109934-109934
Closed Access | Times Cited: 6
Predicting compressive strength in cement mortar: The impact of fly ash composition through machine learning
Navaratnarajah Sathiparan
Sustainable Chemistry and Pharmacy (2025) Vol. 43, pp. 101915-101915
Closed Access
Advanced and hybrid machine learning techniques for predicting compressive strength in palm oil fuel ash-modified concrete with SHAP analysis
Tariq Ali, Kennedy C. Onyelowe, Muhammad Sarmad Mahmood, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A comparative analysis of boosting and genetic programming techniques for predicting mechanical properties of soilcrete materials
Waleed Bin Inqiad, Muhammad Faisal Javed, Muhammad Shahid Siddique, et al.
Materials Today Communications (2024) Vol. 40, pp. 109920-109920
Closed Access | Times Cited: 4
Machine learning models for estimating the compressive strength of rubberized concrete subjected to elevated temperature: Optimization and hyper-tuning
Turki S. Alahmari, Irfan Ullah, Furqan Farooq
Sustainable Chemistry and Pharmacy (2024) Vol. 42, pp. 101763-101763
Closed Access | Times Cited: 4
Utilizing contemporary machine learning techniques for determining soilcrete properties
Waleed Bin Inqiad, Muhammad Saud Khan, Zeeshan Mehmood, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 1
Open Access
Transforming waste rubber gloves into value-added fluorescent carbon quantum dots for direct ink writing
Yanqing Liao, Haitao Ren, Shuhan Liu, et al.
Journal of environmental chemical engineering (2025), pp. 115729-115729
Closed Access
An Explainable Machine Learning (XML) approach to determine strength of glass powder concrete
Wali Ullah Khan, Waleed Bin Inqiad, Bilal Ayub, et al.
Materials Today Communications (2025), pp. 112181-112181
Closed Access
A three-step rockburst prediction model based on data preprocessing combined with clustering and classification algorithms
Hongwei Zhang, Yuanyou Xia, Manqing Lin, et al.
Bulletin of Engineering Geology and the Environment (2024) Vol. 83, Iss. 7
Closed Access | Times Cited: 3
Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP
Waleed Bin Inqiad, Muhammad Shahid Siddique, Mujahid Ali, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Multi-response optimization of thermally efficient RC-based geopolymer binder using Response Surface Methodology Approach
Supriya Janga, Ashwin Raut, Musa Adamu, et al.
Developments in the Built Environment (2024) Vol. 19, pp. 100528-100528
Open Access | Times Cited: 2
Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review
E. Gill, Daniela Cardone, Alessia Amelio
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 1
Interaction and optimisation study of the influence of calcium-carbide-waste, crumb rubber, and fly-ash on the mechanical properties of a developed self-compacting-rubberized concrete for sustainable manufacturing processes
Imhade P. Okokpujie, Lagouge K. Tartibu, Sylvia E. Kelechi, et al.
International Journal on Interactive Design and Manufacturing (IJIDeM) (2024)
Closed Access
Prediction of mechanical properties of bricks manufactured with recycled soil using artificial neural network
R. B. Patel, Neetu Yadav
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 8
Closed Access
Evaluation of Machine Learning and Traditional Methods for Estimating Compressive Strength of UHPC
Tianlong Li, Pengxiao Jiang, Yunfeng Qian, et al.
Buildings (2024) Vol. 14, Iss. 9, pp. 2693-2693
Open Access
Prediction of municipal waste generation using multi-expression programming for circular economy: a data-driven approach
A.S. Olawore, Kuan Yew Wong, K.O. Oladosu
Environmental Science and Pollution Research (2024)
Closed Access
Prediction of Compressive Strength and Design Parameters of C30/37, C35/45 and C40/50 Concrete Classes by ANN
Fatma Kars, Gıyasettin ÖZCAN, Eyyup Gülbandılar, et al.
Journal of Civil Engineering and Urbanism (2024) Vol. 14, Iss. 4, pp. 356-367
Closed Access
Page 1

Scroll to top