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:

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete
Kaffayatullah Khan, Waqas Ahmad, Muhammad Nasir Amin, et al.
Materials (2022) Vol. 15, Iss. 13, pp. 4512-4512
Open Access | Times Cited: 23

Showing 23 citing articles:

Prediction of concrete and FRC properties at high temperature using machine and deep learning: A review of recent advances and future perspectives
Nizar Faisal Alkayem, Lei Shen, Ali Mayya, et al.
Journal of Building Engineering (2023) Vol. 83, pp. 108369-108369
Closed Access | Times Cited: 88
Performance characteristics of cementitious composites modified with silica fume: A systematic review
Yuanfeng Lou, Kaffayatullah Khan, Muhammad Nasir Amin, et al.
Case Studies in Construction Materials (2022) Vol. 18, pp. e01753-e01753
Open Access | Times Cited: 48
A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin
Muhammad Iftikhar Faraz, Siyab Ul Arifeen, Muhammad Nasir Amin, et al.
Structures (2023) Vol. 53, pp. 937-948
Closed Access | Times Cited: 23
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: 23
Unboxing machine learning models for concrete strength prediction using XAI
Sara Elhishi, Asmaa Mohammed Elashry, Sara El‐Metwally
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 22
Advances in formwork automation, structure and materials in concrete construction
Peter Gappmaier, Sara Reichenbach, Benjamin Kromoser
Automation in Construction (2024) Vol. 162, pp. 105373-105373
Open Access | Times Cited: 6
Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete
M. Reshma Anjum, Kaffayatullah Khan, Waqas Ahmad, et al.
Polymers (2022) Vol. 14, Iss. 18, pp. 3906-3906
Open Access | Times Cited: 23
Carbon Emission Optimization of Ultra-High-Performance Concrete Using Machine Learning Methods
Min Wang, Mingfeng Du, Yue Jia, et al.
Materials (2024) Vol. 17, Iss. 7, pp. 1670-1670
Open Access | Times Cited: 4
Intelligent predicting and monitoring of ultra-high-performance fiber reinforced concrete composites − A review
Dingqiang Fan, Ziao Chen, Yuan Cao, et al.
Composites Part A Applied Science and Manufacturing (2024), pp. 108555-108555
Closed Access | Times Cited: 3
Emerging Trends of Ergonomic Risk Assessment in Construction Safety Management: A Scientometric Visualization Analysis
Rakhi Vijayakumar, Jaeho Choi
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 23, pp. 16120-16120
Open Access | Times Cited: 16
Self-healing concrete: A scientometric analysis-based review of the research development and scientific mapping
Xiaohong He, Muhammad Nasir Amin, Kaffayatullah Khan, et al.
Case Studies in Construction Materials (2022) Vol. 17, pp. e01521-e01521
Closed Access | Times Cited: 15
In-Depth Analysis of Cement-Based Material Incorporating Metakaolin Using Individual and Ensemble Machine Learning Approaches
Abdulrahman Mohamad Radwan Bulbul, Kaffayatullah Khan, Afnan Nafees, et al.
Materials (2022) Vol. 15, Iss. 21, pp. 7764-7764
Open Access | Times Cited: 14
Machine Learning-Based Prediction Method for Drying Shrinkage of Recycled Aggregate Concrete
Qinghe Wang, Ruihong Dai, Huan Zhang, et al.
Journal of Building Engineering (2024) Vol. 96, pp. 110493-110493
Closed Access | Times Cited: 2
Understanding Structural Changes in Recycled Aggregate Concrete under Thermal Stress
Shuwen Cao, Xubin Cheng, Hui Ran
Buildings (2024) Vol. 14, Iss. 9, pp. 2689-2689
Open Access | Times Cited: 1
On the Use of Machine Learning Technique to Appraise Thermal Properties of Novel Earthen Composite for Sustainable Housing in Sub-Saharan Africa
Assia Aboubakar Mahamat, Moussa Mahamat Boukar
(2024), pp. 161-170
Closed Access | Times Cited: 1
A Machine Learning Led Investigation Predicting the Thermos-mechanical Properties of Novel Waste-based Composite in Construction
Assia Aboubakar Mahamat, Moussa Mahamat Boukar, Nordine Leklou, et al.
Waste and Biomass Valorization (2024) Vol. 15, Iss. 9, pp. 5445-5461
Closed Access | Times Cited: 1
Uso de inteligência artificial para previsão de características de compósitos cimentícios reforçados com fibras: uma revisão sistemática
Yrlles Araujo Moraes, Ádamo Henrique Rocha de Oliveira, Marcelo de Souza Picanço
Caderno Pedagógico (2024) Vol. 21, Iss. 7, pp. e6080-e6080
Open Access
Optimized Design of Low-Carbon Mix Ratio for Non-Dominated Sorting Genetic Algorithm II Concrete Based on Genetic Algorithm-Improved Back Propagation
Fan Zhang, Bo Wen, Ditao Niu, et al.
Materials (2024) Vol. 17, Iss. 16, pp. 4077-4077
Open Access
Fundamental machine learning algorithms and statistical models applied in strength prediction of geopolymers: a systematic review
Jabulani Matsimbe, Megersa Olumana Dinka, David O. Olukanni, et al.
Deleted Journal (2024) Vol. 6, Iss. 10
Open Access
Prediction of Concrete Peak Load and Compressive Failure Strength Using Machine Learning
Tarik Sadat
Key engineering materials (2022) Vol. 938, pp. 235-245
Closed Access | Times Cited: 3
Assessing Concrete Strength Using Machine Learning
Sara Elhishi, A. Elashry, Sara El‐Metwally
(2023)
Closed Access | Times Cited: 1
A Review on Quantum Deep Machine Learning Model for Predicting Rice Husk Ash Compressive Strength
Dorothy Blessing Agboola, Micheal Olaolu Arowolo, Amit Kumar Tyagi
Advances in systems analysis, software engineering, and high performance computing book series (2023), pp. 103-120
Closed Access
Measurement and Prediction of Effect Slenderness Ratios and Aggregate to the Compressive Strength of Concrete by the Core-Drilling Method in Tandem with Machine Learning
Napoli Situmorang, Sofia W. Alisjahbana, Hery Riyanto, et al.
Mathematical Modelling and Engineering Problems (2023) Vol. 10, Iss. 1, pp. 220-226
Open Access
Page 1

Scroll to top