De‐Cheng Feng

186 papers receiving 6.0k citations

Hit Papers

Machine learning-based compressive strength prediction fo...201920262021202320192021202120202023200400600

Peers

De‐Cheng Feng
Comparison fields: 5 of 135
  • Civil and Structural Engineering 5.3k
  • Building and Construction 2.4k
  • Statistics, Probability and Uncertainty 547
  • Mechanics of Materials 494
  • Mechanical Engineering 432
Replace Joan R. Casas with:
Joan R. Casas Spain
Edoardo Cosenza Italy
M. Shahria Alam Canada
Khalid M. Mosalam United States
M.Z. Naser United States
Giovanni Fabbrocino Italy
T. F. Fwa Singapore
Luís Simões da Silva Portugal
Imad L. Al‐Qadi United States
Huaizhi Su China
De‐Cheng Feng relative to Joan R. Casas Spain Joan R. Casas's profile →
Citations per field
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Joan R. Casas · 1×
Citations per year

Countries citing papers authored by De‐Cheng Feng

Since Specialization
Citations

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

Fields of papers citing papers by De‐Cheng Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by De‐Cheng Feng. 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 De‐Cheng Feng. The network helps show where De‐Cheng Feng may publish in the future.

Co-authorship network of co-authors of De‐Cheng Feng

This figure shows the co-authorship network connecting the top 25 collaborators of De‐Cheng Feng. A scholar is included among the top collaborators of De‐Cheng Feng 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 De‐Cheng Feng. De‐Cheng Feng 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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Consistent seismic hazard and fragility analysis considering combined capacity-demand uncertainties via probability density evolution methodbreakdown →
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About De‐Cheng Feng

De‐Cheng Feng is a scholar working on Civil and Structural Engineering, Building and Construction and Statistics, Probability and Uncertainty, having authored 196 papers that have together received 6.2k indexed citations. Recurring topics across this work include Structural Behavior of Reinforced Concrete (71 papers), Seismic Performance and Analysis (69 papers) and Structural Response to Dynamic Loads (52 papers). The work is most often cited by research in Civil and Structural Engineering (5.3k citations), Building and Construction (2.4k citations) and Nuclear Energy and Engineering (38 citations). De‐Cheng Feng has collaborated with scholars based in China, New Zealand and United States. Frequent co-authors include Gang Wu, Xu‐Yang Cao, Sujith Mangalathu, Shi‐Zhi Chen, Xiaodan Ren, Jie Li, Zhongming Jiang, Ertuǧrul Taciroğlu, Zhentao Liu and Xiaodan Wang. Their work appears in journals such as Construction and Building Materials, Computer Methods in Applied Mechanics and Engineering and International Journal for Numerical Methods in Engineering.

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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