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:

Prediction of compressive strength and portland cement composition using cross-validation and feature ranking techniques
Vinay Vakharia, Rajesh Gujar
Construction and Building Materials (2019) Vol. 225, pp. 292-301
Closed Access | Times Cited: 78

Showing 1-25 of 78 citing articles:

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis
Jianfu Xia, Zhifei Wang, Daqing Yang, et al.
Computers in Biology and Medicine (2022) Vol. 143, pp. 105206-105206
Closed Access | Times Cited: 77
Prediction of pull-out behavior of timber glued-in glass fiber reinforced polymer and steel rods under various environmental conditions based on ANN and GEP models
Mostafa Mohammadzadeh Taleshi, Nima Tajik, Alireza Mahmoudian, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02842-e02842
Open Access | Times Cited: 20
An Ultrasonic-AI Hybrid Approach for Predicting Void Defects in Concrete-Filled Steel Tubes via Enhanced XGBoost with Bayesian Optimization
Shuai Wan, Shaozhi Li, George Z. Chen, et al.
Case Studies in Construction Materials (2025), pp. e04359-e04359
Open Access | Times Cited: 4
Correlation analysis and statistical assessment of early hydration characteristics and compressive strength for multi-composite cement paste
Zhiping Li, Xiaojian Gao, Dagang Lü
Construction and Building Materials (2021) Vol. 310, pp. 125260-125260
Closed Access | Times Cited: 59
Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete
Ali H. Naser, Ali H. Badr, Sadiq N. Henedy, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01262-e01262
Open Access | Times Cited: 56
An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs
Alok Sarkar, Md. Maniruzzaman, Mohammad Ashik Alahe, et al.
Journal of Sensors (2023) Vol. 2023, Iss. 1
Open Access | Times Cited: 39
Random forest algorithm and support vector machine for nondestructive assessment of mass moisture content of brick walls in historic buildings
Anna Hoła, Sławomir Czarnecki
Automation in Construction (2023) Vol. 149, pp. 104793-104793
Closed Access | Times Cited: 30
Ensemble XGBoost schemes for improved compressive strength prediction of UHPC
May Huu Nguyen, Thuy‐Anh Nguyen, Haï-Bang Ly
Structures (2023) Vol. 57, pp. 105062-105062
Closed Access | Times Cited: 30
Strength prediction and optimization for ultrahigh-performance concrete with low-carbon cementitious materials – XG boost model and experimental validation
M. Iqbal Khan, Yassir M. Abbas, Galal Fares, et al.
Construction and Building Materials (2023) Vol. 387, pp. 131606-131606
Closed Access | Times Cited: 27
Compressive strength prediction of ternary-blended concrete using deep neural network with tuned hyperparameters
J. S. Choi, Dongyoun Kim, Minsam Ko, et al.
Journal of Building Engineering (2023) Vol. 75, pp. 107004-107004
Closed Access | Times Cited: 24
Landscape fragmentation and regularity lead to decreased carbon stocks in basins: Evidence from century-scale research
Yinglong Hou, Lingxia Wang, Zhongwu Li, et al.
Journal of Environmental Management (2024) Vol. 367, pp. 121937-121937
Closed Access | Times Cited: 9
Benchmarking AutoML solutions for concrete strength prediction: Reliability, uncertainty, and dilemma
Mohammad Amin Hariri‐Ardebili, Parsa Mahdavi, Farhad Pourkamali‐Anaraki
Construction and Building Materials (2024) Vol. 423, pp. 135782-135782
Closed Access | Times Cited: 8
Generalized uncertainty in surrogate models for concrete strength prediction
Mohammad Amin Hariri‐Ardebili, Golsa Mahdavi
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106155-106155
Closed Access | Times Cited: 20
Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs)
Samuel J. Belfield, Mark T.D. Cronin, Steven J. Enoch, et al.
PLoS ONE (2023) Vol. 18, Iss. 5, pp. e0282924-e0282924
Open Access | Times Cited: 19
Enhancing prediction accuracy of workability and compressive strength of high-performance concrete through extended dataset and improved machine learning models
Rupesh Kumar Tipu, Suman Suman, Vandna Batra
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 197-218
Closed Access | Times Cited: 18
Machine learning-enabled characterization of concrete mechanical strength through correlation of flexural and torsional resonance frequencies
Bai Li, Majid Samavatian, Vahid Samavatian
Physica Scripta (2024) Vol. 99, Iss. 7, pp. 076002-076002
Closed Access | Times Cited: 6
Prediction of Compressive Strength of Concrete with Manufactured Sand by Ensemble Classification and Regression Tree Method
Qiang Ren, Luchuan Ding, Xiaodi Dai, et al.
Journal of Materials in Civil Engineering (2021) Vol. 33, Iss. 7
Closed Access | Times Cited: 33
Analyzing the Relationship between Compressive Strength and Modulus of Elasticity in Concrete with Ladle Furnace Slag
Víctor Revilla‐Cuesta, Roberto Serrano-López, Ana B. Espinosa, et al.
Buildings (2023) Vol. 13, Iss. 12, pp. 3100-3100
Open Access | Times Cited: 15
A machine learning approach for condition monitoring of high voltage insulators in polluted environments
Héctor de Santos, Miguel Á. Sanz-Bobi
Electric Power Systems Research (2023) Vol. 220, pp. 109340-109340
Closed Access | Times Cited: 14
Classification of geogrid reinforcement in aggregate using machine learning techniques
Samuel Olamide Aregbesola, Yong‐Hoon Byun
International Journal of Geo-Engineering (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 5
Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete
Sarmad Dashti Latif
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 46, pp. 65935-65944
Closed Access | Times Cited: 27
Machine learning-based prediction of compressive strength for limestone calcined clay cements
Yassine El Khessaimi, Youssef El Hafiane, Agnès Smith, et al.
Journal of Building Engineering (2023) Vol. 76, pp. 107062-107062
Open Access | Times Cited: 12
A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology
Samsuryadi Samsuryadi, Rudi Kurniawan, Julian Supardi, et al.
Journal of Electrical and Computer Engineering (2023) Vol. 2023, pp. 1-15
Open Access | Times Cited: 11
PREDICTION OF CONCRETE MIXTURE DESIGN AND COMPRESSIVE STRENGTH THROUGH DATA ANALYSIS AND MACHINE LEARNING
Mohammad Hematibahar
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES (2024) Vol. 19, Iss. 3
Open Access | Times Cited: 4
Inversion of soil organic carbon content based on the two-point machine learning method
Chenyi Wang, Bingbo Gao, Ke Yang, et al.
The Science of The Total Environment (2024) Vol. 943, pp. 173608-173608
Closed Access | Times Cited: 4
Page 1 - Next Page

Scroll to top