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:

Estimating compressive strength of lightweight foamed concrete using neural, genetic and ensemble machine learning approaches
Babatunde Abiodun Salami, Mudassir Iqbal, Abdulazeez Abdulraheem, et al.
Cement and Concrete Composites (2022) Vol. 133, pp. 104721-104721
Open Access | Times Cited: 99

Showing 1-25 of 99 citing articles:

Prediction and optimization model of sustainable concrete properties using machine learning, deep learning and swarm intelligence: A review
Shiqi Wang, Peng Xia, Keyu Chen, et al.
Journal of Building Engineering (2023) Vol. 80, pp. 108065-108065
Closed Access | Times Cited: 65
Multi objective optimization of recycled aggregate concrete based on explainable machine learning
Shiqi Wang, Peng Xia, Fuyuan Gong, et al.
Journal of Cleaner Production (2024) Vol. 445, pp. 141045-141045
Closed Access | Times Cited: 54
Active roof-contact: The future development of cemented paste backfill
Shenghua Yin, Zepeng Yan, Xun Chen, et al.
Construction and Building Materials (2023) Vol. 370, pp. 130657-130657
Closed Access | Times Cited: 48
Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
Mohamed Abdellatief, Leong Sing Wong, Norashidah Md Din, et al.
Materials Today Communications (2024) Vol. 40, pp. 110022-110022
Closed Access | Times Cited: 30
Predictive models in machine learning for strength and life cycle assessment of concrete structures
A. Dinesh, B. Rahul Prasad
Automation in Construction (2024) Vol. 162, pp. 105412-105412
Closed Access | Times Cited: 19
Sustainable foam glass property prediction using machine learning: A comprehensive comparison of predictive methods and techniques
Mohamed Abdellatief, Leong Sing Wong, Norashidah Md Din, et al.
Results in Engineering (2025), pp. 104089-104089
Open Access | Times Cited: 12
Carbon capture and storage CO2 foam concrete towards higher performance: Design, preparation and characteristics
Dingqiang Fan, Jian‐Xin Lu, Xuesen Lv, et al.
Cement and Concrete Composites (2025) Vol. 157, pp. 105925-105925
Closed Access | Times Cited: 2
Intelligent mix design of recycled brick aggregate concrete based on swarm intelligence
Shiqi Wang, Peng Xia, Zhao Wang, et al.
Journal of Building Engineering (2023) Vol. 71, pp. 106508-106508
Closed Access | Times Cited: 42
Prediction of mechanical properties of recycled aggregate fly ash concrete employing machine learning algorithms
Maedeh Hosseinzadeh, Mehdi Dehestani, Alireza Hosseinzadeh
Journal of Building Engineering (2023) Vol. 76, pp. 107006-107006
Closed Access | Times Cited: 42
A state-of-the-art review on geopolymer foam concrete with solid waste materials: components, characteristics, and microstructure
Mohamed Abdellatief, Mohamed Abd Elrahman, Hani Alanazi, et al.
Innovative Infrastructure Solutions (2023) Vol. 8, Iss. 9
Closed Access | Times Cited: 35
Development of compressive strength prediction platform for concrete materials based on machine learning techniques
Kexin Liu, Lingyan Zhang, Weiguang Wang, et al.
Journal of Building Engineering (2023) Vol. 80, pp. 107977-107977
Closed Access | Times Cited: 30
Towards white box modeling of compressive strength of sustainable ternary cement concrete using explainable artificial intelligence (XAI)
Syed Muhammad Ibrahim, Saad Shamim Ansari, Syed Danish Hasan
Applied Soft Computing (2023) Vol. 149, pp. 110997-110997
Closed Access | Times Cited: 26
Experimental investigation into the self-carbonation mechanism of magnesium oxide carbon sequestration foamed concrete
Xiang Zhang, Songyu Liu, Kai Wu, et al.
Cement and Concrete Composites (2024) Vol. 148, pp. 105486-105486
Closed Access | Times Cited: 14
A comparative analysis of tree-based machine learning algorithms for predicting the mechanical properties of fibre-reinforced GGBS geopolymer concrete
Shimol Philip, M. Nidhi, Hemn Unis Ahmed
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 3, pp. 2555-2583
Closed Access | Times Cited: 13
Comparative Analysis of Gradient-Boosting Ensembles for Estimation of Compressive Strength of Quaternary Blend Concrete
Ismail B. Mustapha, Muyideen Abdulkareem, Taha M. Jassam, et al.
International Journal of Concrete Structures and Materials (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 12
Compressive strength and sensitivity analysis of fly ash composite foam concrete: Efficient machine learning approach
Chen Zhang, Zhiduo Zhu, Liang Shi, et al.
Advances in Engineering Software (2024) Vol. 192, pp. 103634-103634
Closed Access | Times Cited: 11
A systematic literature review of AI-based prediction methods for self-compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete
Tariq Ali, Mohamed Hechmi El Ouni, Muhammad Zeeshan Qureshi, et al.
Construction and Building Materials (2024) Vol. 440, pp. 137370-137370
Closed Access | Times Cited: 11
High-performance lightweight foam concrete enabled by compositing ultra-stable hydrophobic aqueous foam
Xuesen Lv, Wenxiang Cao, Marcus Yio, et al.
Cement and Concrete Composites (2024) Vol. 152, pp. 105675-105675
Closed Access | Times Cited: 10
BPNN-assisted control algorithm application to a super large cable-net structure of active FAST reflector system
Juncai Liu, Zhen Ma, Li Tian, et al.
Structures (2025) Vol. 73, pp. 108467-108467
Closed Access | Times Cited: 1
A review on properties and multi-objective performance predictions of concrete based on machine learning models
Bowen Ni, Md Zillur Rahman, Shuaicheng Guo, et al.
Materials Today Communications (2025), pp. 112017-112017
Closed Access | Times Cited: 1
Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend
Ramin Kazemi
Engineering Reports (2023) Vol. 5, Iss. 9
Open Access | Times Cited: 22
Building energy loads prediction using bayesian-based metaheuristic optimized-explainable tree-based model
Babatunde Abiodun Salami, Sani I. Abba, Adeshina Adewale Adewumi, et al.
Case Studies in Construction Materials (2023) Vol. 19, pp. e02676-e02676
Open Access | Times Cited: 20
Reinforcement bond performance in 3D concrete printing: Explainable ensemble learning augmented by deep generative adversarial networks
Xianlin Wang, Nemkumar Banthia, Doo‐Yeol Yoo
Automation in Construction (2023) Vol. 158, pp. 105164-105164
Closed Access | Times Cited: 20
Slope stability analysis of heavy-haul freight corridor using novel machine learning approach
Md Shayan Sabri, Furquan Ahmad, Pijush Samui
Modeling Earth Systems and Environment (2023) Vol. 10, Iss. 1, pp. 201-219
Closed Access | Times Cited: 17
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
Page 1 - Next Page

Scroll to top