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:

Development of a prediction tool for the compressive strength of ternary blended ultra-high performance concrete using machine learning techniques
Rakesh Kumar, Shubhum Prakash, Baboo Rai, et al.
Journal of Structural Integrity and Maintenance (2024) Vol. 9, Iss. 3
Closed Access | Times Cited: 16

Showing 16 citing articles:

Comparative analysis of sloshing effects on elevated water tanks’ dynamic response using ANN and MARS
Tahera, Neethu Urs, Shashi Raj K, et al.
Discover Materials (2025) Vol. 5, Iss. 1
Open Access | Times Cited: 4
Analyzing the influence of manufactured sand and fly ash on concrete strength through experimental and machine learning methods
S. Sathvik, Solomon Oyebisi, Rakesh Kumar, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 4
Deep Learning and Genetic Programming-Based Soft-Computing Prediction Models for Metakaolin Mortar
Manish Kumar, Divesh Ranjan Kumar, Warit Wipulanusat, et al.
Transportation Infrastructure Geotechnology (2025) Vol. 12, Iss. 1
Closed Access | Times Cited: 3
Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach
Rakesh Kumar, Shishir Karthik, Abhishek Kumar, et al.
Discover Materials (2025) Vol. 5, Iss. 1
Open Access | Times Cited: 3
Assessing the seismic sensitivity of bridge structures by developing fragility curves with ANN and LSTM integration
Ashwini Satyanarayana, V. Babu R. Dushyanth, Khaja Asim Riyan, et al.
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 8, pp. 5865-5888
Closed Access | Times Cited: 9
Comparison of experimental and analytical studies in light gauge steel sections on CFST using SFRC in beams subjected to high temperatures
Christo George, Rakesh Kumar, H. K. Ramaraju
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 9
Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability
S. Sathvik, Rakesh Kumar, Archudha Arjunasamy, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Prediction of moisture damage properties of asphalt mixtures using machine learning models
Shiva Kumar G, Nitin Goel, G. Gurudeep, et al.
Journal of Structural Integrity and Maintenance (2025) Vol. 10, Iss. 2
Closed Access | Times Cited: 1
Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm
Somdutta, Baboo Rai
Nondestructive Testing And Evaluation (2025), pp. 1-33
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
Estimation of the Compressive Strength of Ultrahigh Performance Concrete using Machine Learning Models
Rakesh Kumar, Divesh Ranjan Kumar, Warit Wipulanusat, et al.
Intelligent Systems with Applications (2024) Vol. 25, pp. 200471-200471
Open Access | Times Cited: 8
Enhancing urban sustainability: a study on lightweight and pervious concrete incorporating recycled plastic
S. Sathvik, Pathapati Rohithkumar, Pshtiwan Shakor, et al.
Discover Sustainability (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 4
Investigating the Impact of Biaxial Geogrid Reinforcement on Subgrade Soil Strength Enhancement: A Machine Learning Analysis Using the MARS Model
M. Harshitha, Rakesh Kumar, J. C. Vidyashree, et al.
Indian geotechnical journal (2025)
Closed Access
Evaluating the feasibility of using iron powder as a partial replacement for fine aggregates in concrete: An AI-based modeling approach
M. Harshitha, U.S. Agrawal, S. Sathvik, et al.
Construction and Building Materials (2025) Vol. 474, pp. 140890-140890
Closed Access
Prediction of compressive strength of blended concrete with Alccofine and GGBFS by applying ensemble machine learning algorithms
A. Punitha, C. Vivek Kumar, R. Swetha, et al.
Journal of Structural Integrity and Maintenance (2025) Vol. 10, Iss. 2
Closed Access
A multifaceted comparative analysis of incremental dynamic and static pushover methods in bridge structural assessment, integrated with artificial neural network and genetic algorithm approach
Ashwini Satyanarayana, V. Sindura, L. Geetha, et al.
Discover Materials (2025) Vol. 5, Iss. 1
Open Access
Optimizing beam performance: ANSYS simulation and ANN-based analysis of CFRP strengthening with various opening shapes
Tahera, Kshitij S. Patil, Neethu Urs
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 1
Page 1

Scroll to top