Anish Kumar

692 total citations
24 papers, 496 citations indexed

About

Anish Kumar is a scholar working on Civil and Structural Engineering, Building and Construction and Pollution. According to data from OpenAlex, Anish Kumar has authored 24 papers receiving a total of 496 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Civil and Structural Engineering, 9 papers in Building and Construction and 4 papers in Pollution. Recurrent topics in Anish Kumar's work include Concrete and Cement Materials Research (10 papers), Innovative concrete reinforcement materials (10 papers) and Smart Materials for Construction (3 papers). Anish Kumar is often cited by papers focused on Concrete and Cement Materials Research (10 papers), Innovative concrete reinforcement materials (10 papers) and Smart Materials for Construction (3 papers). Anish Kumar collaborates with scholars based in India and Nepal. Anish Kumar's co-authors include Debabrata Das, Kaushik Nath, Sanjeev Sinha, M. Muthukumar, Vinay Bhushan Chauhan, Abhishek Kumar Srivastava, Rajiv Manohar, Swadesh Kumar Gupta, Sujit Sen and C. Suresh Kumar and has published in prestigious journals such as Scientific Reports, International Journal of Hydrogen Energy and Applied Microbiology and Biotechnology.

In The Last Decade

Anish Kumar

23 papers receiving 478 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anish Kumar India 11 290 146 127 121 89 24 496
K-Y Show Singapore 5 284 1.0× 146 1.0× 95 0.7× 8 0.1× 69 0.8× 7 351
Jingfang Ding China 6 202 0.7× 120 0.8× 79 0.6× 4 0.0× 63 0.7× 9 374
Srivastava Manish India 4 267 0.9× 214 1.5× 139 1.1× 3 0.0× 104 1.2× 8 516
Jixu Tang China 8 52 0.2× 241 1.7× 48 0.4× 53 0.4× 4 0.0× 11 502

Countries citing papers authored by Anish Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Anish Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anish Kumar

This figure shows the co-authorship network connecting the top 25 collaborators of Anish Kumar. A scholar is included among the top collaborators of Anish Kumar 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 Anish Kumar. Anish Kumar 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.
Kumar, Anish, Sujit Sen, & Sanjeev Sinha. (2025). Machine learning based prediction models for the compressive strength of high-volume fly ash concrete reinforced with silica fume. Asian Journal of Civil Engineering. 26(4). 1683–1701. 4 indexed citations
2.
Kumar, Anish & Sanjeev Sinha. (2025). Machine learning-based hybrid regularization techniques for predicting unconfined compressive strength of soil reinforced with multiple additives. Multiscale and Multidisciplinary Modeling Experiments and Design. 8(5). 4 indexed citations
3.
Kumar, Anish, et al.. (2025). Prediction of compressive strength of concrete doped with waste plastic using machine learning-based advanced regularized regression models. Asian Journal of Civil Engineering. 26(4). 1723–1741. 3 indexed citations
4.
Singh, Manish Pratap, Anish Kumar, & Sanjeev Sinha. (2025). Machine learning-based prediction of compressive strength of plastic optical fiber embedded transparent concrete. Asian Journal of Civil Engineering. 26(6). 2527–2545. 3 indexed citations
5.
Kumar, Anish, Sanjeev Sinha, & Rojee Pradhananga. (2025). Assessment of unconfined compressive strength of nano-doped fly ash-treated clayey soil using machine learning tools. Scientific Reports. 15(1). 33847–33847.
6.
Kumar, Anish, Sujit Sen, & Sanjeev Sinha. (2025). Support vector machine-based prediction model for the compressive strength for concrete reinforced with waste plastic and fly ash. Asian Journal of Civil Engineering. 26(4). 1429–1447. 8 indexed citations
7.
Kumar, Anish & Sanjeev Sinha. (2024). Support vector machine-based prediction of unconfined compressive strength of Multi-Walled Carbon nanotube doped soil-fly ash mixes. Multiscale and Multidisciplinary Modeling Experiments and Design. 7(6). 5365–5386. 11 indexed citations
8.
Kumar, Anish, et al.. (2023). Prediction of unconfined compressive strength of cement–fly ash stabilized soil using support vector machines. Asian Journal of Civil Engineering. 25(2). 1149–1161. 27 indexed citations
9.
Kumar, Anish, et al.. (2023). Random forest, CART, and MLR-based predictive model for unconfined compressive strength of cement reinforced clayey soil: a comparative analysis. Asian Journal of Civil Engineering. 25(2). 2307–2323. 16 indexed citations
10.
Chauhan, Vinay Bhushan, et al.. (2023). Machine learning-based stability assessment of unlined circular tunnels under surcharge loading. Asian Journal of Civil Engineering. 25(3). 2553–2566. 7 indexed citations
11.
Kumar, Anish & Sanjeev Sinha. (2023). Multiwalled Carbon Nanotube Aided Fly Ash-Based Subgrade Soil Stabilization for Low-Volume Rural Roads. International Journal of Geosynthetics and Ground Engineering. 9(2). 13 indexed citations
12.
Kumar, Anish & Sanjeev Sinha. (2023). Role of Multiwalled Carbon Nanotube in the Improvement of Compaction and Strength Characteristics of Fly Ash Stabilized Soil. International Journal of Pavement Research and Technology. 17(4). 868–889. 16 indexed citations
13.
Kumar, Anish, et al.. (2023). Advanced regression models for assessing the strength of multi-walled carbon nanotube-modified high-volume fly ash concrete. Asian Journal of Civil Engineering. 25(2). 2247–2268. 8 indexed citations
14.
Kumar, Anish, et al.. (2022). Performance of multiwalled carbon nanotube doped fly ash based clay bricks. International Journal of Advanced Technology and Engineering Exploration. 9(89). 7 indexed citations
15.
16.
Kumar, Anish, et al.. (2021). Water quality assessment of Ganga River along its course in India. Innovative Infrastructure Solutions. 7(1). 6 indexed citations
17.
Ramnath, B. Vijaya, et al.. (2014). Analysis and Optimization of Gating System for Commutator End Bracket. Procedia Materials Science. 6. 1312–1328. 13 indexed citations
18.
Nath, Kaushik, M. Muthukumar, Anish Kumar, & Debabrata Das. (2008). Kinetics of two-stage fermentation process for the production of hydrogen. International Journal of Hydrogen Energy. 33(4). 1195–1203. 146 indexed citations
19.
Nath, Kaushik, Anish Kumar, & Debabrata Das. (2006). Effect of some environmental parameters on fermentative hydrogen production by Enterobacter cloacae DM11. Canadian Journal of Microbiology. 52(6). 525–532. 67 indexed citations
20.
Nath, Kaushik, Anish Kumar, & Debabrata Das. (2005). Hydrogen production by Rhodobacter sphaeroides strain O.U.001 using spent media of Enterobacter cloacae strain DM11. Applied Microbiology and Biotechnology. 68(4). 533–541. 97 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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