Farzin Kazemi
About
In The Last Decade
Farzin Kazemi
42 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 89
- Civil and Structural Engineering 1.3k
- Building and Construction 384
- Artificial Intelligence 119
- Mechanics of Materials 112
- Mechanical Engineering 102
Countries citing papers authored by Farzin Kazemi
This map shows the geographic impact of Farzin Kazemi'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 Farzin Kazemi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Farzin Kazemi more than expected).
Fields of papers citing papers by Farzin Kazemi
This network shows the impact of papers produced by Farzin Kazemi. 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 Farzin Kazemi. The network helps show where Farzin Kazemi may publish in the future.
Co-authorship network of co-authors of Farzin Kazemi
This figure shows the co-authorship network connecting the top 25 collaborators of Farzin Kazemi. A scholar is included among the top collaborators of Farzin Kazemi 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 Farzin Kazemi. Farzin Kazemi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 8 | |
| 4 | RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials breakdown → | 41 |
| 5 | Grey wolf optimizer integrated within boosting algorithm: Application in mechanical properties prediction of ultra high-performance concrete including carbon nanotubes breakdown → | 43 |
| 6 | Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete breakdown → | 119 |
| 7 | 22 | |
| 8 | Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review breakdown → | 74 |
| 9 | Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers breakdown → | 57 |
| 10 | 48 | |
| 11 | 12 | |
| 12 | 23 | |
| 13 | Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods breakdown → | 98 |
| 14 | Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures breakdown → | 176 |
| 15 | 17 | |
| 16 | 16 | |
| 17 | Machine learning-based seismic response and performance assessment of reinforced concrete buildings breakdown → | 130 |
| 18 | 1 | |
| 19 | 29 | |
| 20 | 51 |
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.