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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:

LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis
Bin Xi, Enming Li, Yewuhalashet Fissha, et al.
Mechanics of Advanced Materials and Structures (2023) Vol. 31, Iss. 23, pp. 5999-6014
Closed Access | Times Cited: 25

Showing 25 citing articles:

Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses
Abul Kashem, Rezaul Karim, Somir Chandra Malo, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e02991-e02991
Open Access | Times Cited: 46
Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses
Abul Kashem, Rezaul Karim, Pobithra Das, et al.
Case Studies in Construction Materials (2024) Vol. 20, pp. e03030-e03030
Open Access | Times Cited: 32
Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning
Vu Hong Son Pham, Nghiep Trinh Nguyen Dang, Nguyễn Văn Nam
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 18
Combined influence of modified recycled concrete aggregate and metakaolin on high-strength concrete production: Experimental assessment and machine learning quantifications with advanced SHAP and PDP analyses
Md. Habibur Rahman Sobuz, Md Mushfiqur Rahman, Rahat Aayaz, et al.
Construction and Building Materials (2025) Vol. 461, pp. 139897-139897
Closed Access | Times Cited: 8
A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis
Pobithra Das, Abul Kashem, Imrul Hasan, et al.
Asian Journal of Civil Engineering (2024) Vol. 25, Iss. 4, pp. 3301-3316
Closed Access | Times Cited: 13
Vibration analysis of embedded porous nanobeams under thermal effects using boosting machine learning algorithms and semi-analytical approach
Aiman Tariq, Büşra Uzun, Babür Deliktaş, et al.
Mechanics of Advanced Materials and Structures (2024), pp. 1-24
Closed Access | Times Cited: 13
Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms
Yewuhalashet Fissha, Jitendra Khatti, Hajime Ikeda, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 12
BIM Integration with XAI Using LIME and MOO for Automated Green Building Energy Performance Analysis
Abdul Mateen Khan, Muhammad Abubakar Tariq, Sardar Kashif Ur Rehman, et al.
Energies (2024) Vol. 17, Iss. 13, pp. 3295-3295
Open Access | Times Cited: 10
A comparative study of ensemble machine learning models for compressive strength prediction in recycled aggregate concrete and parametric analysis
Pobithra Das, Abul Kashem, Jasim Uddin Rahat, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 3457-3482
Closed Access | Times Cited: 6
Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach
Angesom Gebretsadik, Rahul Kumar, Yewuhalashet Fissha, et al.
Deleted Journal (2024) Vol. 6, Iss. 5
Open Access | Times Cited: 6
Quantifying compressive strength in limestone powder incorporated concrete with incorporating various machine learning algorithms with SHAP analysis
Mihir Mishra
Asian Journal of Civil Engineering (2024)
Closed Access | Times Cited: 5
1DCNN-based prediction methods for subsequent settlement of subgrade with limited monitoring data
Senlin Xie, Anfeng Hu, Meihui Wang, et al.
European Journal of Environmental and Civil engineering (2025), pp. 1-26
Closed Access
Machine learning approaches for resilient modulus modeling of cement-stabilized magnetite and hematite iron ore tailings
Farzad Safi Jahanshahi, Ali Reza Ghanizadeh
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Prediction of compressive strength of high-performance concrete using optimization machine learning approaches with SHAP analysis
Md Mahamodul Islam, Pobithra Das, Md Mahbubur Rahman, et al.
Journal of Building Pathology and Rehabilitation (2024) Vol. 9, Iss. 2
Closed Access | Times Cited: 4
Harnessing Synergy of Machine Learning and Nature-Inspired Optimization for Enhanced Compressive Strength Prediction in Concrete
Abba Bashir, Esar Ahmad, Shashivendra Dulawat, et al.
Hybrid Advances (2025), pp. 100404-100404
Open Access
Experimental assessment and hybrid machine learning-based feature importance analysis with the optimization of compressive strength of waste glass powder-modified concrete
Turki S. Alahmari, Md. Kawsarul Islam Kabbo, Md. Habibur Rahman Sobuz, et al.
Materials Today Communications (2025), pp. 112081-112081
Closed Access
New insights into the performance of biomass carbon-based supercapacitors based on interpretable machine learning approach
Pengfei Liu, Ge Yu, Huanhuan Li, et al.
Journal of Energy Storage (2025) Vol. 118, pp. 116300-116300
Closed Access
Explainable machine learning model for predicting compressive strength of CO2-cured concrete
Jia Chu, Bingbing Guo, Taotao Zhong, et al.
Case Studies in Construction Materials (2025), pp. e04589-e04589
Open Access
Effective carbon footprint assessment strategy in fly ash geopolymer concrete based on adaptive boosting learning techniques
Y.S. Wudil, Amin Al‐Fakih, Mohammed A. Al‐Osta, et al.
Environmental Research (2024), pp. 120570-120570
Closed Access | Times Cited: 3
Evaluation and Prediction of Blast-Induced Ground Vibrations: A Gaussian Process Regression (GPR) Approach
Yewuhalashet Fissha, Hajime Ikeda, Hisatoshi Toriya, et al.
Mining (2023) Vol. 3, Iss. 4, pp. 659-682
Open Access | Times Cited: 8
Analysis and modelling of gas relative permeability in reservoir by hybrid KELM methods
Enming Li, Ning Zhang, Bin Xi, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 4, pp. 3163-3190
Closed Access | Times Cited: 2
Mean Block Size Prediction in Rock Blast Fragmentation Using TPE-Tree-Based Model Approach with SHapley Additive exPlanations
Madalitso Mame, Yingui Qiu, Shuai Huang, et al.
Mining Metallurgy & Exploration (2024) Vol. 41, Iss. 5, pp. 2325-2340
Closed Access | Times Cited: 2
Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining
Yewuhalashet Fissha, Prashanth Ragam, Hajime Ikeda, et al.
Rock Mechanics Bulletin (2024), pp. 100166-100166
Open Access | Times Cited: 2
Explainable machine learning model for predicting molten steel temperature in the LF refining process
Zicheng Xin, Jiangshan Zhang, Kaixiang Peng, et al.
International Journal of Minerals Metallurgy and Materials (2024) Vol. 31, Iss. 12, pp. 2657-2669
Closed Access | Times Cited: 2
A Comprehensive Study on the Estimation of Concrete Compressive Strength Using Machine Learning Models
Yusuf Tahir ALTUNCI
Buildings (2024) Vol. 14, Iss. 12, pp. 3851-3851
Open Access | Times Cited: 1
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