Muhammad Sufian

1.1k total citations · 1 hit paper
25 papers, 724 citations indexed

About

Muhammad Sufian is a scholar working on Civil and Structural Engineering, Building and Construction and Electrical and Electronic Engineering. According to data from OpenAlex, Muhammad Sufian has authored 25 papers receiving a total of 724 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Civil and Structural Engineering, 13 papers in Building and Construction and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Muhammad Sufian's work include Innovative concrete reinforcement materials (17 papers), Concrete and Cement Materials Research (14 papers) and Structural Behavior of Reinforced Concrete (5 papers). Muhammad Sufian is often cited by papers focused on Innovative concrete reinforcement materials (17 papers), Concrete and Cement Materials Research (14 papers) and Structural Behavior of Reinforced Concrete (5 papers). Muhammad Sufian collaborates with scholars based in China, Saudi Arabia and Egypt. Muhammad Sufian's co-authors include Ahmed Farouk Deifalla, Fahid Aslam, Fadi Althoey, Ayaz Ahmad, Wajahat Sammer Ansari, Syed Hassan Farooq, Waqas Ahmad, Muhammad Usman, Mehran Khan and Asad Zia and has published in prestigious journals such as PLoS ONE, Aquaculture and Sustainability.

In The Last Decade

Muhammad Sufian

23 papers receiving 699 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Advancements in low-carbon concrete as a construction material for the sustainable built environment

2023 105 citations Fadi Althoey, Muhammad Sufian et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Muhammad Sufian China	13	590	437	89	39	32	25	724
Jaime Gonzalez‐Libreros Sweden	11	532 0.9×	444 1.0×	88 1.0×	29 0.7×	28 0.9×	46	703
Chaohua Jiang China	11	739 1.3×	518 1.2×	54 0.6×	72 1.8×	31 1.0×	26	835
Tidarut Jirawattanasomkul Thailand	13	481 0.8×	425 1.0×	125 1.4×	14 0.4×	22 0.7×	31	600
M. Abdullahi Nigeria	11	628 1.1×	506 1.2×	36 0.4×	37 0.9×	17 0.5×	17	726
Oladimeji B. Olalusi South Africa	19	706 1.2×	532 1.2×	55 0.6×	97 2.5×	20 0.6×	45	849
Ertuğ Aydın Türkiye	14	491 0.8×	327 0.7×	32 0.4×	76 1.9×	34 1.1×	27	579
Swaptik Chowdhury India	7	657 1.1×	552 1.3×	76 0.9×	43 1.1×	20 0.6×	12	786
Soner Güler Türkiye	22	910 1.5×	631 1.4×	49 0.6×	73 1.9×	27 0.8×	44	1.0k
Eethar Thanon Dawood Iraq	17	747 1.3×	519 1.2×	96 1.1×	138 3.5×	21 0.7×	70	869
Fahed Alrshoudi Saudi Arabia	17	603 1.0×	398 0.9×	61 0.7×	106 2.7×	34 1.1×	37	714

Countries citing papers authored by Muhammad Sufian

Since Specialization

Citations

This map shows the geographic impact of Muhammad Sufian's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Muhammad Sufian with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Sufian more than expected).

Fields of papers citing papers by Muhammad Sufian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muhammad Sufian. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Muhammad Sufian. The network helps show where Muhammad Sufian may publish in the future.

Co-authorship network of co-authors of Muhammad Sufian

This figure shows the co-authorship network connecting the top 25 collaborators of Muhammad Sufian. A scholar is included among the top collaborators of Muhammad Sufian based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Muhammad Sufian. Muhammad Sufian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhang, Lu, et al.. (2025). Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete. Case Studies in Construction Materials. 22. e04754–e04754.

Tu, Yongming, et al.. (2025). Experimental and explainable machine learning based investigation of the coal bottom ash replacement in sustainable concrete production. Journal of Building Engineering. 104. 112367–112367. 5 indexed citations

Chang, Qiuying, et al.. (2025). AI-powered optimization of engineered cementitious composites properties and CO₂ emissions for sustainable construction. Case Studies in Construction Materials. 22. e04405–e04405. 4 indexed citations

Ghaffar, Abdul, Samra, H Jamil, et al.. (2024). Growth performance, blood health, and antioxidant status of freshwater carp under brackish water rearing system: Sustainable aquaculture approach in arid and semi-arid conditions. Aquaculture. 596. 741691–741691. 5 indexed citations

Sufian, Muhammad, et al.. (2024). Prediction of Ultra-High-Performance Concrete (UHPC) Properties Using Gene Expression Programming (GEP). Buildings. 14(9). 2675–2675. 10 indexed citations

Li, Tianlong, et al.. (2024). Predicting High-Strength Concrete’s Compressive Strength: A Comparative Study of Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Response Surface Methodology. Materials. 17(18). 4533–4533. 9 indexed citations

Cooper, Bernard R., et al.. (2024). Development of a Low Thermal Expansion Nanocomposite Resin for MSLA 3D Printer.

Zhao, Jun, Muhammad Sufian, Mohammed Awad Abuhussain, Fadi Althoey, & Ahmed Farouk Deifalla. (2024). Exploring the potential of agricultural waste as an additive in ultra-high-performance concrete for sustainable construction: A comprehensive review. REVIEWS ON ADVANCED MATERIALS SCIENCE. 63(1). 8 indexed citations

Li, Tianlong, et al.. (2024). Evaluation of Machine Learning and Traditional Methods for Estimating Compressive Strength of UHPC. Buildings. 14(9). 2693–2693. 3 indexed citations

10.

Iftikhar, Bawar, Ayaz Ahmad, Yakubu Aminu Dodo, et al.. (2023). Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models. Materials Today Communications. 37. 107356–107356. 13 indexed citations

11.

Yang, Jianyu, et al.. (2023). Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction. Journal of Materials Research and Technology. 23. 3676–3696. 32 indexed citations

12.

Zheng, Wei, Muhammad Nasir Amin, Kaffayatullah Khan, Muhammad Sufian, & Ahmed Farouk Deifalla. (2023). A scientometric review of the literature on the incorporation of steel fibers in ultra-high-performance concrete with research mapping knowledge. REVIEWS ON ADVANCED MATERIALS SCIENCE. 62(1). 6 indexed citations

13.

Sufian, Muhammad, et al.. (2023). Application of machine learning algorithms to evaluate the influence of various parameters on the flexural strength of ultra-high-performance concrete. Frontiers in Materials. 9. 21 indexed citations

14.

Zhao, Yanjie, et al.. (2023). Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods. Open Geosciences. 15(1). 7 indexed citations

15.

Bahrami, Alireza, et al.. (2023). Machine learning-based evaluation of parameters of high-strength concrete and raw material interaction at elevated temperatures. Frontiers in Materials. 10. 9 indexed citations

16.

Zia, Asad, Pu Zhang, Ivan Hollý, et al.. (2022). A Comprehensive Review of Incorporating Steel Fibers of Waste Tires in Cement Composites and Its Applications. Materials. 15(21). 7420–7420. 27 indexed citations

17.

Sufian, Muhammad, et al.. (2022). Advanced machine learning algorithms to evaluate the effects of the raw ingredients on flowability and compressive strength of ultra-high-performance concrete. PLoS ONE. 17(12). e0278161–e0278161. 12 indexed citations

18.

Li, Yongjian, Qizhi Zhang, Paweł Kamiński, et al.. (2022). Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques. Materials. 15(12). 4209–4209. 58 indexed citations

19.

Sufian, Muhammad, et al.. (2021). An Experimental and Empirical Study on the Use of Waste Marble Powder in Construction Material. Materials. 14(14). 3829–3829. 76 indexed citations

20.

Ahmad, Waqas, Syed Hassan Farooq, Muhammad Usman, et al.. (2020). Effect of Coconut Fiber Length and Content on Properties of High Strength Concrete. Materials. 13(5). 1075–1075. 159 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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