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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:

Data-Driven Compressive Strength Prediction of Fly Ash Concrete Using Ensemble Learner Algorithms
Mohammad Sadegh Barkhordari, Danial Jahed Armaghani, Ahmed Salih Mohammed, et al.
Buildings (2022) Vol. 12, Iss. 2, pp. 132-132
Open Access | Times Cited: 82

Showing 26-50 of 82 citing articles:

A Novel Combination of PCA and Machine Learning Techniques to Select the Most Important Factors for Predicting Tunnel Construction Performance
Jiangfeng Wang, Ahmed Salih Mohammed, Elżbieta Macioszek, et al.
Buildings (2022) Vol. 12, Iss. 7, pp. 919-919
Open Access | Times Cited: 36
Microstructure, chemical compositions, and soft computing models to evaluate the influence of silicon dioxide and calcium oxide on the compressive strength of cement mortar modified with cement kiln dust
Aso A. Abdalla, Ahmed Salih Mohammed, Serwan Rafiq, et al.
Construction and Building Materials (2022) Vol. 341, pp. 127668-127668
Closed Access | Times Cited: 30
Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests
Yuzhen Wang, Mahdi Hasanipanah, Ahmad Safuan A. Rashid, et al.
Materials (2023) Vol. 16, Iss. 10, pp. 3731-3731
Open Access | Times Cited: 23
Modelling the compressive strength of high-performance concrete containing metakaolin using distinctive statistical techniques
B. Sankar, P. Ramadoss
Results in Control and Optimization (2023) Vol. 12, pp. 100241-100241
Open Access | Times Cited: 22
Mixed artificial intelligence models for compressive strength prediction and analysis of fly ash concrete
Wei Liang, Wei Yin, Yu Zhong, et al.
Advances in Engineering Software (2023) Vol. 185, pp. 103532-103532
Closed Access | Times Cited: 22
Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar
Zhengyu Fei, Shixue Liang, Yiqing Cai, et al.
Materials (2023) Vol. 16, Iss. 2, pp. 583-583
Open Access | Times Cited: 18
Mechanical behaviour of E-waste aggregate concrete using a novel machine learning algorithm: Multi expression programming (MEP)
Sultan Shah, Moustafa Houda, Sangeen Khan, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 5720-5740
Open Access | Times Cited: 17
Efficacy of high-volume fly ash and slag on the physicomechanical, durability, and analytical characteristics of high-strength mass concrete
Manish Prabhakar Mokal, Romio Mandal, Sanket Nayak, et al.
Journal of Building Engineering (2023) Vol. 76, pp. 107295-107295
Closed Access | Times Cited: 17
ICA-LightGBM Algorithm for Predicting Compressive Strength of Geo-Polymer Concrete
Qiang Wang, Jiali Qi, Shahab Hosseini, et al.
Buildings (2023) Vol. 13, Iss. 9, pp. 2278-2278
Open Access | Times Cited: 17
Compressive Strength Prediction of Fly Ash Concrete Using Machine Learning Techniques
Yimin Jiang, Hangyu Li, Yisong Zhou
Buildings (2022) Vol. 12, Iss. 5, pp. 690-690
Open Access | Times Cited: 27
A hybrid model based on convolution neural network and long short-term memory for qualitative assessment of permeable and porous concrete
Manish Kumar, Manish Kumar, Shatakshi Singh, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02254-e02254
Open Access | Times Cited: 14
Accurate compressive strength prediction using machine learning algorithms and optimization techniques
Wenbin Lan
Journal of Engineering and Applied Science (2024) Vol. 71, Iss. 1
Open Access | Times Cited: 6
A comprehensive study on the impact of human hair fiber and millet husk ash on concrete properties: response surface modeling and optimization
Naraindas Bheel, Muhammad Alamgeer Shams, Samiullah Sohu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures
Alireza Javid, Vahab Toufigh
Structural Concrete (2024) Vol. 25, Iss. 5, pp. 4048-4074
Closed Access | Times Cited: 5
Reliability Analysis of RC Slab-Column Joints under Punching Shear Load Using a Machine Learning-Based Surrogate Model
Lulu Shen, Yuanxie Shen, Shixue Liang
Buildings (2022) Vol. 12, Iss. 10, pp. 1750-1750
Open Access | Times Cited: 20
Prediction of the concrete compressive strength using improved random forest algorithm
Mohammad Khodaparasti, Ali Alijamaat, Majid Pouraminian
Journal of Building Pathology and Rehabilitation (2023) Vol. 8, Iss. 2
Closed Access | Times Cited: 13
Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks
Irina Razveeva, Alexey Kozhakin, Alexey N. Beskopylny, et al.
Buildings (2023) Vol. 13, Iss. 12, pp. 3014-3014
Open Access | Times Cited: 12
Machine Learning Technique for the Prediction of Blended Concrete Compressive Strength
Dawood S. A. Jubori, Nabilah Abu Bakar, Nor Azizi Safiee, et al.
KSCE Journal of Civil Engineering (2024) Vol. 28, Iss. 2, pp. 817-835
Closed Access | Times Cited: 4
Mechanical properties of solid waste-based composite cementitious system enhanced by CO2 modification
Dedan Duan, Huiping Song, Wei Fang, et al.
Construction and Building Materials (2024) Vol. 426, pp. 136187-136187
Closed Access | Times Cited: 4
Nano-bentonite as a sustainable enhancer for alkali activated nano concrete: Assessing mechanical, microstructural, and sustainable properties
Samuvel Raj R, G. Prince Arulraj, N. Anand, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03213-e03213
Open Access | Times Cited: 4
Prediction of Mechanical Properties of Highly Functional Lightweight Fiber-Reinforced Concrete Based on Deep Neural Network and Ensemble Regression Trees Methods
Sergey A. Stel’makh, Evgenii M. Shcherban’, Alexey N. Beskopylny, et al.
Materials (2022) Vol. 15, Iss. 19, pp. 6740-6740
Open Access | Times Cited: 19
Prediction of compressive strength in plain and blended cement concretes using a hybrid artificial intelligence model
Hamdi A. Al-Jamimi, Walid A. Al-Kutti, Saleh Alwahaishi, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01238-e01238
Closed Access | Times Cited: 16
The Efficiency of Hybrid Intelligent Models in Predicting Fiber-Reinforced Polymer Concrete Interfacial-Bond Strength
Mohammad Sadegh Barkhordari, Danial Jahed Armaghani, Mohanad Muayad Sabri Sabri, et al.
Materials (2022) Vol. 15, Iss. 9, pp. 3019-3019
Open Access | Times Cited: 15
The Time Variation Law of Concrete Compressive Strength: A Review
Weina Wang, Qingxia Yue
Applied Sciences (2023) Vol. 13, Iss. 8, pp. 4947-4947
Open Access | Times Cited: 9
A review of soft computing techniques in predicting the compressive strength of concrete and the future scope
Tanvesh Dabholkar, Harish Narayana, Prashanth Janardhan
Innovative Infrastructure Solutions (2023) Vol. 8, Iss. 6
Closed Access | Times Cited: 9

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