Seunguk Na

711 total citations · 1 hit paper
25 papers, 466 citations indexed

About

Seunguk Na is a scholar working on Building and Construction, Civil and Structural Engineering and Environmental Engineering. According to data from OpenAlex, Seunguk Na has authored 25 papers receiving a total of 466 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Building and Construction, 9 papers in Civil and Structural Engineering and 9 papers in Environmental Engineering. Recurrent topics in Seunguk Na's work include Recycled Aggregate Concrete Performance (11 papers), Environmental Impact and Sustainability (7 papers) and Sustainable Building Design and Assessment (6 papers). Seunguk Na is often cited by papers focused on Recycled Aggregate Concrete Performance (11 papers), Environmental Impact and Sustainability (7 papers) and Sustainable Building Design and Assessment (6 papers). Seunguk Na collaborates with scholars based in South Korea and United Kingdom. Seunguk Na's co-authors include Seokjae Heo, Sehee Han, Yoon-Soo Shin, Young-Sook Roh, Seungho Cho, Junhee Kim and Sang‐Hyun Lee and has published in prestigious journals such as IEEE Access, International Journal of Environmental Research and Public Health and Sustainability.

In The Last Decade

Seunguk Na

25 papers receiving 445 citations

Hit Papers

Acceptance Model of Artif... 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework
    2022 152 citations Seunguk Na, Seokjae Heo et al. Buildings profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seunguk Na South Korea 13 200 124 91 66 61 25 466
Syed Imran Ali United Kingdom 9 98 0.5× 264 2.1× 18 0.2× 47 0.7× 26 0.4× 17 612
Sofiat Abioye United Kingdom 3 290 1.4× 93 0.8× 8 0.1× 48 0.7× 132 2.2× 4 561
Ahmed H. Ibrahim Egypt 16 314 1.6× 96 0.8× 12 0.1× 32 0.5× 337 5.5× 56 605
Alireza Adibfar United States 6 315 1.6× 215 1.7× 13 0.1× 19 0.3× 71 1.2× 11 510
Mengqi Yuan China 11 250 1.3× 20 0.2× 29 0.3× 14 0.2× 192 3.1× 21 491
Perry Forsythe Australia 16 356 1.8× 74 0.6× 10 0.1× 13 0.2× 271 4.4× 46 626
Yuhan Niu Hong Kong 10 328 1.6× 57 0.5× 26 0.3× 9 0.1× 185 3.0× 13 496
Arjen Adriaanse Netherlands 11 522 2.6× 76 0.6× 20 0.2× 13 0.2× 386 6.3× 18 639

Countries citing papers authored by Seunguk Na

Since Specialization
Citations

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

Fields of papers citing papers by Seunguk Na

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seunguk Na

This figure shows the co-authorship network connecting the top 25 collaborators of Seunguk Na. A scholar is included among the top collaborators of Seunguk Na 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 Seunguk Na. Seunguk Na 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.
Na, Seunguk, et al.. (2024). Integrating Drone Imagery and AI for Improved Construction Site Management through Building Information Modeling. Buildings. 14(4). 1106–1106. 12 indexed citations
2.
Heo, Seokjae & Seunguk Na. (2024). Developing WasteSAM: A novel approach for accurate construction waste image segmentation to facilitate efficient recycling. Waste Management & Research The Journal for a Sustainable Circular Economy. 43(7). 1048–1059. 2 indexed citations
3.
Cho, Seungho, et al.. (2024). Shear Reinforcement Effectiveness of One-Way Void Slab with the Hollow Core Ratio and Shear Reinforcement. Applied Sciences. 14(11). 4737–4737. 1 indexed citations
4.
Heo, Seokjae, et al.. (2023). Serviceability Evaluation of High-Rise Buildings Exposed to Typhoon Proximity Effects Using ISO10137 and ISO6897. Buildings. 13(8). 2119–2119. 1 indexed citations
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Heo, Seokjae, et al.. (2022). The Modification of A* Pathfinding Algorithm for Building Mechanical, Electronic and Plumbing (MEP) Path. IEEE Access. 10. 65784–65800. 7 indexed citations
8.
Na, Seunguk, et al.. (2022). Comparison of the Performance of Artificial Intelligence Models Depending on the Labelled Image by Different User Levels. Applied Sciences. 12(6). 3136–3136. 8 indexed citations
9.
Na, Seunguk, Seokjae Heo, Sehee Han, Yoon-Soo Shin, & Young-Sook Roh. (2022). Acceptance Model of Artificial Intelligence (AI)-Based Technologies in Construction Firms: Applying the Technology Acceptance Model (TAM) in Combination with the Technology–Organisation–Environment (TOE) Framework. Buildings. 12(2). 90–90. 152 indexed citations breakdown →
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Heo, Seokjae, Seunguk Na, Sehee Han, Yoon-Soo Shin, & Sang‐Hyun Lee. (2021). Flip Side of Artificial Intelligence Technologies: New Labor-Intensive Industry of the 21st Century. Journal of the Computational Structural Engineering Institute of Korea. 34(5). 327–337. 2 indexed citations
12.
Heo, Seokjae, Sehee Han, Yoon-Soo Shin, & Seunguk Na. (2021). Challenges of Data Refining Process during the Artificial Intelligence Development Projects in the Architecture, Engineering and Construction Industry. Applied Sciences. 11(22). 10919–10919. 18 indexed citations
13.
Shin, Yoon-Soo, et al.. (2021). An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography. Buildings. 11(10). 463–463. 20 indexed citations
14.
Na, Seunguk, Seokjae Heo, & Sehee Han. (2021). Construction Waste Reduction through Application of Different Structural Systems for the Slab in a Commercial Building: A South Korean Case. Applied Sciences. 11(13). 5870–5870. 12 indexed citations
15.
Han, Sehee, et al.. (2020). Evaluation of Road Transport Pollutant Emissions from Transporting Building Materials to the Construction Site by Replacing Old Vehicles. International Journal of Environmental Research and Public Health. 17(24). 9316–9316. 5 indexed citations
16.
Na, Seunguk, et al.. (2019). Evaluation of Carbon Dioxide Emissions amongst Alternative Slab Systems during the Construction Phase in a Building Project. Applied Sciences. 9(20). 4333–4333. 13 indexed citations
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Na, Seunguk, et al.. (2019). Application of Thermal Image Data to Detect Rebar Corrosion in Concrete Structures. Applied Sciences. 9(21). 4700–4700. 13 indexed citations
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20.
Cho, Seungho & Seunguk Na. (2018). Evaluation of the Flexural Performance and CO2 Emissions of the Voided Slab. Advances in Materials Science and Engineering. 2018(1). 8 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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