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:

Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms
Hong‐Gen Chen, Xin Li, Yanqi Wu, et al.
Buildings (2022) Vol. 12, Iss. 3, pp. 302-302
Open Access | Times Cited: 58

Showing 1-25 of 58 citing articles:

Hybrid machine learning model and Shapley additive explanations for compressive strength of sustainable concrete
Yanqi Wu, Yisong Zhou
Construction and Building Materials (2022) Vol. 330, pp. 127298-127298
Closed Access | Times Cited: 128
Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations
Pobithra Das, Abul Kashem
Case Studies in Construction Materials (2023) Vol. 20, pp. e02723-e02723
Open Access | Times Cited: 44
Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm
M. Aminul Haque, Bing Chen, Abul Kashem, et al.
Materials Today Communications (2023) Vol. 35, pp. 105547-105547
Closed Access | Times Cited: 41
Assessing the compressive strength of self-compacting concrete with recycled aggregates from mix ratio using machine learning approach
P. Jagadesh, Jesús de Prado-Gil, Neemias Silva-Monteiro, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 1483-1498
Open Access | Times Cited: 39
Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation
Md. Habibur Rahman Sobuz, Limon Paul Joy, Abu Sayed Mohammad Akid, et al.
Heliyon (2024) Vol. 10, Iss. 6, pp. e27793-e27793
Open Access | Times Cited: 27
Advanced modeling for predicting compressive strength in fly ash-modified recycled aggregate concrete: XGboost, MEP, MARS, and ANN approaches
Brwa Omer, Dilshad Kakasor Ismael Jaf, Aso A. Abdalla, et al.
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 3
Closed Access | Times Cited: 19
Interpretable Machine Learning Framework to Predict the Glass Transition Temperature of Polymers
Md. Jamal Uddin, Jitang Fan
Polymers (2024) Vol. 16, Iss. 8, pp. 1049-1049
Open Access | Times Cited: 14
Splitting tensile strength prediction of sustainable high-performance concrete using machine learning techniques
Yanqi Wu, Yisong Zhou
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 59, pp. 89198-89209
Closed Access | Times Cited: 42
Prediction and feature analysis of punching shear strength of two-way reinforced concrete slabs using optimized machine learning algorithm and Shapley additive explanations
Yanqi Wu, Yisong Zhou
Mechanics of Advanced Materials and Structures (2022) Vol. 30, Iss. 15, pp. 3086-3096
Closed Access | Times Cited: 42
Compressive strength prediction of high-strength concrete using hybrid machine learning approaches by incorporating SHAP analysis
Abul Kashem, Pobithra Das
Asian Journal of Civil Engineering (2023) Vol. 24, Iss. 8, pp. 3243-3263
Closed Access | Times Cited: 35
Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars
Ning Wang, Majid Samavatian, Vahid Samavatian, et al.
Materials Today Communications (2023) Vol. 35, pp. 106283-106283
Closed Access | Times Cited: 25
Investigating the applicability of deep learning and machine learning models in predicting the structural performance of V-shaped RC folded plates
Metin Katlav, Faruk Ergen, Kâzım Türk, et al.
Materials Today Communications (2024) Vol. 38, pp. 108141-108141
Closed Access | Times Cited: 10
Predicting the compressive strength of fiber-reinforced self-consolidating concrete using a hybrid machine learning approach
Turki S. Alahmari, Jawad Ashraf, Md. Habibur Rahman Sobuz, et al.
Innovative Infrastructure Solutions (2024) Vol. 9, Iss. 11
Closed Access | Times Cited: 7
Advanced machine learning prediction of the unconfined compressive strength of geopolymer cement reconstituted granular sand for road and liner construction applications
Kennedy C. Onyelowe, Ahmed M. Ebid, Shadi Hanandeh
Asian Journal of Civil Engineering (2023) Vol. 25, Iss. 1, pp. 1027-1041
Closed Access | Times Cited: 20
A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh, et al.
Technologies (2023) Vol. 11, Iss. 2, pp. 55-55
Open Access | Times Cited: 16
Predicting engineering properties of controlled low-strength material made from waste soil using optimized SVR models
Guijie Zhao, Xiaoqiang Pan, Huan Yan, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03325-e03325
Open Access | Times Cited: 6
Prediction on the freeze-thaw resistance of a one-part geopolymer stabilized soil by using deep learning method
Chuanqin Yao, Guo Hu, Qinyi Chen, et al.
Case Studies in Construction Materials (2024) Vol. 21, pp. e03530-e03530
Open Access | Times Cited: 5
Using AI-Based Tools to Quantify the Technical Condition of Bridge Structural Components
Roman Trach, Volodymyr Tyvoniuk, Tomasz Wierzbicki, et al.
Applied Sciences (2025) Vol. 15, Iss. 3, pp. 1625-1625
Open Access
Stratified Metamodeling to Predict Concrete Compressive Strength Using an Optimized Dual-Layered Architectural Framework
Geraldo F. Neto, Bruno da S. Macêdo, Tales Humberto de Aquino Boratto, et al.
Mathematical and Computational Applications (2025) Vol. 30, Iss. 1, pp. 16-16
Open Access
Machine Learning Approaches for Predicting Mechanical Properties of Steel-Fiber-Reinforced Concrete
Huan Huang, Long Lei, Guoxin Xu, et al.
Materials Today Communications (2025), pp. 112149-112149
Closed Access
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: 26
Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis
Ying-Lei Lin, Chi-Ju Lai, Ping‐Feng Pai
Electronics (2022) Vol. 11, Iss. 21, pp. 3513-3513
Open Access | Times Cited: 22
Alkali-activated binder concrete strength prediction using hybrid-deep learning along with shapely additive explanations and uncertainty analysis
Pobithra Das, Abul Kashem, Mominul Islam, et al.
Construction and Building Materials (2024) Vol. 435, pp. 136711-136711
Closed Access | Times Cited: 4
Assessment of Machine Learning Models for the Prediction of Rate-Dependent Compressive Strength of Rocks
Ziquan Yang, Yanqi Wu, Yisong Zhou, et al.
Minerals (2022) Vol. 12, Iss. 6, pp. 731-731
Open Access | Times Cited: 19
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: 19
Page 1 - Next Page

Scroll to top