Majid Khan

1.2k total citations · 2 hit papers
33 papers, 824 citations indexed

About

Majid Khan is a scholar working on Civil and Structural Engineering, Building and Construction and Automotive Engineering. According to data from OpenAlex, Majid Khan has authored 33 papers receiving a total of 824 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Civil and Structural Engineering, 18 papers in Building and Construction and 3 papers in Automotive Engineering. Recurrent topics in Majid Khan's work include Innovative concrete reinforcement materials (20 papers), Innovations in Concrete and Construction Materials (10 papers) and Concrete and Cement Materials Research (7 papers). Majid Khan is often cited by papers focused on Innovative concrete reinforcement materials (20 papers), Innovations in Concrete and Construction Materials (10 papers) and Concrete and Cement Materials Research (7 papers). Majid Khan collaborates with scholars based in Pakistan, Saudi Arabia and United States. Majid Khan's co-authors include Yaser Gamil, Taoufik Najeh, Mujahid Ali, Hisham Alabduljabbar, Muhammad Fawad, Muhammad Faisal Javed, Muhammad Faisal Javed, Roz‐Ud‐Din Nassar, Ali Aldrees and Rayed Alyousef and has published in prestigious journals such as Scientific Reports, Journal of Materials Research and Technology and Heliyon.

In The Last Decade

Majid Khan

28 papers receiving 790 citations

Hit Papers

Evaluation of water quali... 2024 2026 2024 2024 25 50 75
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.

  1. Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches
    2024 87 citations Ali Aldrees, Majid Khan et al. Journal of Water Process Engineering profile →
  2. Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
    2024 58 citations Roz‐Ud‐Din Nassar, Majid Khan et al. Case Studies in Construction Materials profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Majid Khan Pakistan 17 563 350 63 62 53 33 824
Sadi Ibrahim Haruna Saudi Arabia 18 713 1.3× 416 1.2× 106 1.7× 28 0.5× 39 0.7× 68 951
Aydin Shishegaran Iran 17 462 0.8× 223 0.6× 69 1.1× 35 0.6× 14 0.3× 32 647
Mahmoud Akbari Iran 13 466 0.8× 265 0.8× 64 1.0× 48 0.8× 42 0.8× 24 773
Muhammad Imran Khan Malaysia 25 1.2k 2.1× 440 1.3× 54 0.9× 83 1.3× 43 0.8× 89 1.7k
Raid Alrowais Saudi Arabia 15 300 0.5× 252 0.7× 71 1.1× 124 2.0× 38 0.7× 40 685
Taoufik Najeh Sweden 16 338 0.6× 198 0.6× 28 0.4× 24 0.4× 76 1.4× 51 625
Huzaifa Hashim Malaysia 14 485 0.9× 241 0.7× 55 0.9× 35 0.6× 41 0.8× 42 649
Shaban Ismael Albrka Ali‬ Cyprus 16 579 1.0× 98 0.3× 77 1.2× 66 1.1× 36 0.7× 56 815
George Uwadiegwu Alaneme Uganda 24 880 1.6× 627 1.8× 43 0.7× 17 0.3× 51 1.0× 84 1.2k

Countries citing papers authored by Majid Khan

Since Specialization
Citations

This map shows the geographic impact of Majid Khan'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 Majid Khan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Majid Khan more than expected).

Fields of papers citing papers by Majid Khan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Majid Khan. 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 Majid Khan. The network helps show where Majid Khan may publish in the future.

Co-authorship network of co-authors of Majid Khan

This figure shows the co-authorship network connecting the top 25 collaborators of Majid Khan. A scholar is included among the top collaborators of Majid Khan 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 Majid Khan. Majid Khan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Khan, Asad U., Muhammad Faisal Javed, & Majid Khan. (2025). Development of prediction models for strength properties of concrete using gene expression programming. Innovative Infrastructure Solutions. 10(3).
3.
4.
Khan, Majid, et al.. (2025). Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation. Discover Civil Engineering. 2(1). 2 indexed citations
5.
Javed, Muhammad Faisal, Deema Mohammed Alsekait, Naseer Muhammad Khan, et al.. (2025). Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete. Scientific Reports. 15(1). 10740–10740.
6.
Arif, Muhammad, et al.. (2024). Data-driven models for predicting compressive strength of 3D-printed fiber-reinforced concrete using interpretable machine learning algorithms. Case Studies in Construction Materials. 21. e03935–e03935. 14 indexed citations
7.
Aldrees, Ali, et al.. (2024). Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms. Journal of Water Process Engineering. 66. 105937–105937. 7 indexed citations
8.
Chen, Li, Qadir Bux alias Imran Latif, Deema Mohammed Alsekait, et al.. (2024). Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures. Composites Part C Open Access. 15. 100529–100529. 5 indexed citations
9.
Khan, Majid, Mujahid Ali, Taoufik Najeh, & Yaser Gamil. (2024). Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming. Scientific Reports. 14(1). 6105–6105. 25 indexed citations
10.
Aldrees, Ali, et al.. (2024). Leveraging machine learning to model salinity and water flux for improved insights into forward osmosis membrane bioreactors. Journal of Water Process Engineering. 68. 106585–106585. 3 indexed citations
11.
Ma, Liqiang, et al.. (2024). Predicting residual strength of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) exposed to elevated temperatures using machine learning. Case Studies in Construction Materials. 22. e04112–e04112. 10 indexed citations
12.
Javed, Muhammad Faisal, Majid Khan, Muhammad Fawad, et al.. (2024). Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand. Scientific Reports. 14(1). 14617–14617. 20 indexed citations
13.
Aldrees, Ali, et al.. (2024). Evaluation of water quality indexes with novel machine learning and SHapley Additive ExPlanation (SHAP) approaches. Journal of Water Process Engineering. 58. 104789–104789. 87 indexed citations breakdown →
14.
Javed, Muhammad Faisal, et al.. (2024). Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants. Results in Engineering. 23. 102637–102637. 21 indexed citations
15.
Javed, Muhammad Faisal, et al.. (2024). Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures. Materials Today Communications. 39. 108832–108832. 12 indexed citations
16.
Khan, Majid & Muhammad Faisal Javed. (2023). Towards sustainable construction: Machine learning based predictive models for strength and durability characteristics of blended cement concrete. Materials Today Communications. 37. 107428–107428. 44 indexed citations
17.
Khan, Majid, et al.. (2023). Predictive modeling for depth of wear of concrete modified with fly ash: A comparative analysis of genetic programming-based algorithms. Case Studies in Construction Materials. 20. e02744–e02744. 24 indexed citations
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19.
Khan, Majid, et al.. (2023). Optimizing durability assessment: Machine learning models for depth of wear of environmentally-friendly concrete. Results in Engineering. 20. 101625–101625. 42 indexed citations
20.
Khan, Majid, et al.. (2022). A Review on Fiber-Reinforced Foam Concrete. MDPI (MDPI AG). 13–13. 15 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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