Kang Tai
- Civil and Structural Engineering top 0.5%
- Mechanical Engineering top 2%
- Computational Theory and Mathematics top 0.5%
- Artificial Intelligence top 2%
- Mechanics of Materials top 2%
- Co-authors
- Akhil GargTapabrata RayS.Y. WangV. VijayaraghavanAnand J. KulkarniC.H. WongAnkit GargC. Y. Teresa Lam
- Topics
- Topology Optimization in Engineering (30 papers)Advanced Multi-Objective Optimization Algorithms (28 papers)Metaheuristic Optimization Algorithms Research (17 papers)
In The Last Decade
Kang Tai
170 papers receiving 4.0k citations
Peers
Comparison fields: 5 of 183
- Civil and Structural Engineering 1.2k
- Mechanical Engineering 909
- Computational Theory and Mathematics 889
- Artificial Intelligence 724
- Mechanics of Materials 541
Countries citing papers authored by Kang Tai
This map shows the geographic impact of Kang Tai'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 Kang Tai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kang Tai more than expected).
Fields of papers citing papers by Kang Tai
This network shows the impact of papers produced by Kang Tai. 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 Kang Tai. The network helps show where Kang Tai may publish in the future.
Co-authorship network of co-authors of Kang Tai
This figure shows the co-authorship network connecting the top 25 collaborators of Kang Tai. A scholar is included among the top collaborators of Kang Tai 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 Kang Tai. Kang Tai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 9 | |
| 3 | 0 | |
| 4 | 7 | |
| 5 | 12 | |
| 6 | 32 | |
| 7 | 3 | |
| 8 | 12 | |
| 9 | 1 | |
| 10 | Structural optimization and parametric study of offshore wind turbine jacket substructure | 9 |
| 11 | Comparison of regression analysis, Artificial Neural Network and genetic programming in Handling the multicollinearity problem | 35 |
| 12 | 54 | |
| 13 | 115 | |
| 14 | 56 | |
| 15 | 65 | |
| 16 | 63 | |
| 17 | 27 | |
| 18 | 37 | |
| 19 | 49 | |
| 20 | 0 |
About Kang Tai
Kang Tai is a scholar working on Civil and Structural Engineering, Statistics, Probability and Uncertainty and Computational Theory and Mathematics, having authored 176 papers that have together received 4.2k indexed citations. Recurring topics across this work include Topology Optimization in Engineering (30 papers), Advanced Multi-Objective Optimization Algorithms (28 papers) and Metaheuristic Optimization Algorithms Research (17 papers). The work is most often cited by research in Civil and Structural Engineering (1.2k citations), Computational Theory and Mathematics (889 citations) and Industrial and Manufacturing Engineering (360 citations). Kang Tai has collaborated with scholars based in Singapore, China and India. Frequent co-authors include Akhil Garg, Tapabrata Ray, S.Y. Wang, V. Vijayaraghavan, Anand J. Kulkarni, C.H. Wong, Ankit Garg, C. Y. Teresa Lam, M. M. Savalani and M. Y. Wang. Their work appears in journals such as Applied Physics Letters, Gastroenterology and Journal of Applied Physics.
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.