Mujib Rahman

1.2k total citations
37 papers, 859 citations indexed

About

Mujib Rahman is a scholar working on Civil and Structural Engineering, Computer Vision and Pattern Recognition and Industrial and Manufacturing Engineering. According to data from OpenAlex, Mujib Rahman has authored 37 papers receiving a total of 859 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Civil and Structural Engineering, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Industrial and Manufacturing Engineering. Recurrent topics in Mujib Rahman's work include Infrastructure Maintenance and Monitoring (27 papers), Asphalt Pavement Performance Evaluation (22 papers) and Image and Object Detection Techniques (5 papers). Mujib Rahman is often cited by papers focused on Infrastructure Maintenance and Monitoring (27 papers), Asphalt Pavement Performance Evaluation (22 papers) and Image and Object Detection Techniques (5 papers). Mujib Rahman collaborates with scholars based in United Kingdom, Pakistan and Jordan. Mujib Rahman's co-authors include Senthan Mathavan, K. Kamal, Khurram Kamal, Chawnshang Chang, Hiroshi Miyamoto, Sahnius Usman, Henry A. Lardy, Paritosh Bhattacharya, Mazen J. Al‐Kheetan and Hiroshi Takatera and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Construction and Building Materials.

In The Last Decade

Mujib Rahman

35 papers receiving 817 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mujib Rahman United Kingdom 14 668 111 100 89 88 37 859
Wenlong Ye China 17 296 0.4× 245 2.2× 114 1.1× 25 0.3× 32 0.4× 34 763
Junjie Chen China 17 266 0.4× 231 2.1× 19 0.2× 43 0.5× 35 0.4× 94 883
Takafumi Nishikawa Japan 11 381 0.6× 79 0.7× 72 0.7× 34 0.4× 52 0.6× 34 611
Semin Oh South Korea 7 165 0.2× 47 0.4× 34 0.3× 22 0.2× 46 0.5× 23 391
Yicheng Li China 5 240 0.4× 52 0.5× 34 0.3× 53 0.6× 33 0.4× 22 386
Wenting Luo China 12 212 0.3× 42 0.4× 96 1.0× 25 0.3× 35 0.4× 31 404
Yiwen Cao China 6 566 0.8× 152 1.4× 24 0.2× 76 0.9× 57 0.6× 17 691
Liam Butler United Kingdom 18 987 1.5× 92 0.8× 23 0.2× 13 0.1× 93 1.1× 58 1.7k
Zhangyu Wang China 14 87 0.1× 68 0.6× 134 1.3× 22 0.2× 25 0.3× 58 620

Countries citing papers authored by Mujib Rahman

Since Specialization
Citations

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

Fields of papers citing papers by Mujib Rahman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mujib Rahman

This figure shows the co-authorship network connecting the top 25 collaborators of Mujib Rahman. A scholar is included among the top collaborators of Mujib Rahman 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 Mujib Rahman. Mujib Rahman 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
1.
Rahman, Mujib, et al.. (2025). Pothole prediction based on machine learning and pavement condition indicators. Proceedings of the Institution of Civil Engineers - Transport. 179(1). 41–50.
2.
Albayati, Amjad H., et al.. (2024). Enhancing Asphalt Performance and Its Long-Term Sustainability with Nano Calcium Carbonate and Nano Hydrated Lime. Sustainability. 16(4). 1507–1507. 15 indexed citations
3.
Rahman, Mujib, et al.. (2023). State of art literature review on the mechanical, functional and long-term performance of cold mix asphalt mixtures. Construction and Building Materials. 400. 132759–132759. 8 indexed citations
4.
Rahman, Mujib, et al.. (2023). Predicting pavement performance using distress deterioration curves. Road Materials and Pavement Design. 25(6). 1174–1190. 6 indexed citations
5.
Al‐Kheetan, Mazen J., et al.. (2023). A Study on Cold Laid Microsurfacing Containing Water-Based Epoxy-Modified Bitumen Emulsion. International Journal of Pavement Research and Technology. 17(4). 1047–1058. 7 indexed citations
6.
Rahman, Mujib, et al.. (2022). Comparing Different Deep Learning Architectures as Vision-Based Multi-Label Classifiers for Identification of Multiple Distresses on Asphalt Pavement. Transportation Research Record Journal of the Transportation Research Board. 2677(5). 24–39. 7 indexed citations
7.
8.
Rahman, Mujib, et al.. (2020). A fuzzy inference system for predicting pavement surface damage due to combined action of traffic loading and water. International Journal of Pavement Engineering. 23(2). 261–269. 1 indexed citations
9.
Mathavan, Senthan, et al.. (2020). A unified artificial neural network model for asphalt pavement condition prediction. Proceedings of the Institution of Civil Engineers - Transport. 176(1). 14–24. 10 indexed citations
10.
Mathavan, Senthan, et al.. (2018). A parameter-free discrete particle swarm algorithm and its application to multi-objective pavement maintenance schemes. Swarm and Evolutionary Computation. 43. 69–87. 20 indexed citations
11.
Evans, Robert D., et al.. (2017). INTERPRETATION OF CONCRETE MIX DESIGNS BY SURFACE HARDNESS METHOD. 29(2). 1 indexed citations
12.
Mathavan, Senthan, et al.. (2017). Detection of pavement cracks using tiled fuzzy Hough transform. Journal of Electronic Imaging. 26(5). 1–1. 18 indexed citations
13.
Kamal, Khurram, et al.. (2016). Performance assessment of Kinect as a sensor for pothole imaging and metrology. International Journal of Pavement Engineering. 19(7). 565–576. 32 indexed citations
14.
Chandrakumar, Chanjief, et al.. (2015). Tiled fuzzy Hough transform for crack detection. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9534. 953411–953411. 5 indexed citations
15.
Mathavan, Senthan, et al.. (2014). Pavement Raveling Detection and Measurement from Synchronized Intensity and Range Images. Transportation Research Record Journal of the Transportation Research Board. 2457(1). 3–11. 37 indexed citations
16.
Mathavan, Senthan, Mujib Rahman, & K. Kamal. (2014). Use of a Self-Organizing Map for Crack Detection in Highly Textured Pavement Images. Journal of Infrastructure Systems. 21(3). 46 indexed citations
17.
Kamal, K., et al.. (2013). Metrology and visualization of potholes using the microsoft kinect sensor. Nottingham Trent University's Institutional Repository (Nottingham Trent Repository). 1284–1291. 85 indexed citations
18.
Mathavan, Senthan, et al.. (2013). Pavement crack detection using the Gabor filter. Nottingham Trent University's Institutional Repository (Nottingham Trent Repository). 2039–2044. 208 indexed citations
19.
Rahman, Mujib & Paritosh Bhattacharya. (2009). An integrated and interactive decision support system for automated melanoma recognition of dermoscopic images. Computerized Medical Imaging and Graphics. 34(6). 479–486. 26 indexed citations
20.
Rahman, Mujib, Hiroshi Miyamoto, Hiroshi Takatera, et al.. (2003). Reducing the Agonist Activity of Antiandrogens by a Dominant-negative Androgen Receptor Coregulator ARA70 in Prostate Cancer Cells. Journal of Biological Chemistry. 278(22). 19619–19626. 34 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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