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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:

Prediction of cement-based mortars compressive strength using machine learning techniques
Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani, et al.
Neural Computing and Applications (2021) Vol. 33, Iss. 19, pp. 13089-13121
Closed Access | Times Cited: 177

Showing 26-50 of 177 citing articles:

A general framework of high-performance machine learning algorithms: application in structural mechanics
George Markou, Nikolaos Bakas, Savvas A. Chatzichristofis, et al.
Computational Mechanics (2024) Vol. 73, Iss. 4, pp. 705-729
Open Access | Times Cited: 11
Assessment of short and long-term pozzolanic activity of natural pozzolans using machine learning approaches
Jitendra Khatti, Berivan Yılmazer Polat
Structures (2024) Vol. 68, pp. 107159-107159
Closed Access | Times Cited: 11
A super-learner machine learning model for a global prediction of compression index in clays
E.F. González Díaz, Giovanni Spagnoli
Applied Clay Science (2024) Vol. 249, pp. 107239-107239
Open Access | Times Cited: 10
Assessment of Uniaxial Strength of Rocks: A Critical Comparison Between Evolutionary and Swarm Optimized Relevance Vector Machine Models
Jitendra Khatti, Kamaldeep Singh Grover
Transportation Infrastructure Geotechnology (2024) Vol. 11, Iss. 6, pp. 4098-4141
Closed Access | Times Cited: 10
Application of various machine learning algorithms in view of predicting the CO2 emissions in the transportation sector
Gökalp Çınarer, Murat Kadir Yeşi̇lyurt, Ümit Ağbulut, et al.
Science and Technology for Energy Transition (2024) Vol. 79, pp. 15-15
Closed Access | Times Cited: 9
Machine learning for predicting compressive strength of sustainable cement paste incorporating copper mine tailings as supplementary cementitious materials
Eka Oktavia Kurniati, Hang Zeng, Marat I. Latypov, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03373-e03373
Open Access | Times Cited: 9
Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants
Muhammad Faisal Javed, Muhammad Zubair Shahab, Usama Asif, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools
Han Sun, Muhammad Nasir Amin, Muhammad Tahir Qadir, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03083-e03083
Open Access | Times Cited: 8
Predicting the compressive strength of CFRP-confined concrete using deep learning
Ali Benzaamia, Mohamed Ghrici, Redouane Rebouh, et al.
Engineering Structures (2024) Vol. 319, pp. 118801-118801
Closed Access | Times Cited: 8
Exploring the viability of AI-aided genetic algorithms in estimating the crack repair rate of self-healing concrete
Qiong Tian, Yijun Lü, Ji Zhou, et al.
REVIEWS ON ADVANCED MATERIALS SCIENCE (2024) Vol. 63, Iss. 1
Open Access | Times Cited: 7
A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates
Navid Kardani, Abidhan Bardhan, Bishwajit Roy, et al.
Engineering With Computers (2021) Vol. 38, Iss. S5, pp. 4323-4346
Closed Access | Times Cited: 49
Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models
Bo Ke, Manoj Khandelwal, Panagiotis G. Asteris, et al.
IEEE Access (2021) Vol. 9, pp. 91347-91360
Open Access | Times Cited: 45
Novel Fuzzy-Based Optimization Approaches for the Prediction of Ultimate Axial Load of Circular Concrete-Filled Steel Tubes
Jin‐song Liao, Panagiotis G. Asteris, Liborio Cavaleri, et al.
Buildings (2021) Vol. 11, Iss. 12, pp. 629-629
Open Access | Times Cited: 44
Prediction compressive strength of cement-based mortar containing metakaolin using explainable Categorical Gradient Boosting model
Ngoc-Hien Nguyen, Tống Tôn Kiên, Seunghye Lee, et al.
Engineering Structures (2022) Vol. 269, pp. 114768-114768
Open Access | Times Cited: 36
The Use of GA and PSO in Evaluating the Shear Strength of Steel Fiber Reinforced Concrete Beams
Jiandong Huang, Mengmeng Zhou, Jia Zhang, et al.
KSCE Journal of Civil Engineering (2022) Vol. 26, Iss. 9, pp. 3918-3931
Closed Access | Times Cited: 35
Development of a New Stacking Model to Evaluate the Strength Parameters of Concrete Samples in Laboratory
Jiandong Huang, Mengmeng Zhou, Jia Zhang, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2022) Vol. 46, Iss. 6, pp. 4355-4370
Closed Access | Times Cited: 33
Artificial intelligence algorithms for predicting peak shear strength of clayey soil-geomembrane interfaces and experimental validation
Zhiming Chao, Danda Shi, Gary Fowmes, et al.
Geotextiles and Geomembranes (2022) Vol. 51, Iss. 1, pp. 179-198
Open Access | Times Cited: 30
Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method
Jiandong Huang, Mengmeng Zhou, Hongwei Yuan, et al.
Materials (2022) Vol. 15, Iss. 10, pp. 3500-3500
Open Access | Times Cited: 28
Hybrid BO-XGBoost and BO-RF Models for the Strength Prediction of Self-Compacting Mortars with Parametric Analysis
Asif Ahmed, Wei Song, Yumeng Zhang, et al.
Materials (2023) Vol. 16, Iss. 12, pp. 4366-4366
Open Access | Times Cited: 19
Soft computing-based prediction models for compressive strength of concrete
Manish Kumar, Rahul Biswas, Divesh Ranjan Kumar, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02321-e02321
Open Access | Times Cited: 19
Estimation of Intact Rock Uniaxial Compressive Strength Using Advanced Machine Learning
Jitendra Khatti, Kamaldeep Singh Grover
Transportation Infrastructure Geotechnology (2023) Vol. 11, Iss. 4, pp. 1989-2022
Closed Access | Times Cited: 19
Machine learning algorithms in wood ash-cement-Nano TiO2-based mortar subjected to elevated temperatures
Akeem Ayinde Raheem, Bolanle Deborah Ikotun, Solomon Oyebisi, et al.
Results in Engineering (2023) Vol. 18, pp. 101077-101077
Open Access | Times Cited: 17
Prediction of the compressive strength of normal concrete using ensemble machine learning approach
Sanjog Chhetri Sapkota, Prasenjit Saha, Sourav Das, et al.
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 583-596
Closed Access | Times Cited: 17
Machine learning prediction of 28-day compressive strength of CNT/cement composites with considering size effects
Jinlong Yang, Yucheng Fan, Fan Zhu, et al.
Composite Structures (2023) Vol. 308, pp. 116713-116713
Closed Access | Times Cited: 16
Prediction of concrete compressive strength using support vector machine regression and non-destructive testing
Wanmao Zhang, Dunwen Liu, Kunpeng Cao
Case Studies in Construction Materials (2024) Vol. 21, pp. e03416-e03416
Open Access | Times Cited: 7

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