Hai‐Van Thi

774 total citations
17 papers, 588 citations indexed

About

Hai‐Van Thi is a scholar working on Civil and Structural Engineering, Building and Construction and Mechanics of Materials. According to data from OpenAlex, Hai‐Van Thi has authored 17 papers receiving a total of 588 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Civil and Structural Engineering, 4 papers in Building and Construction and 2 papers in Mechanics of Materials. Recurrent topics in Hai‐Van Thi's work include Innovative concrete reinforcement materials (14 papers), Infrastructure Maintenance and Monitoring (11 papers) and Concrete and Cement Materials Research (6 papers). Hai‐Van Thi is often cited by papers focused on Innovative concrete reinforcement materials (14 papers), Infrastructure Maintenance and Monitoring (11 papers) and Concrete and Cement Materials Research (6 papers). Hai‐Van Thi collaborates with scholars based in Vietnam, Japan and India. Hai‐Van Thi's co-authors include Hai‐Bang Ly, Van Quan Tran, Thuy‐Anh Nguyen, May Huu Nguyen, Son Hoang Trinh, Thanh-Hai Le, Indra Prakash and Long Hoang Nguyen and has published in prestigious journals such as PLoS ONE, Construction and Building Materials and Neural Computing and Applications.

In The Last Decade

Hai‐Van Thi

17 papers receiving 574 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hai‐Van Thi Vietnam 14 497 233 51 31 26 17 588
May Huu Nguyen Japan 14 491 1.0× 212 0.9× 37 0.7× 46 1.5× 29 1.1× 33 571
Amir Hossein Rafiean Iran 7 665 1.3× 345 1.5× 46 0.9× 39 1.3× 30 1.2× 7 749
Muhammad Fawad Poland 13 286 0.6× 163 0.7× 37 0.7× 24 0.8× 15 0.6× 21 405
Rodrigo Polo-Mendoza Colombia 11 486 1.0× 183 0.8× 68 1.3× 33 1.1× 30 1.2× 25 570
Manish Kewalramani United Arab Emirates 8 460 0.9× 207 0.9× 53 1.0× 47 1.5× 42 1.6× 22 575
Yixin Zhang China 18 404 0.8× 302 1.3× 63 1.2× 54 1.7× 41 1.6× 42 618
Bakhta Boukhatem Algeria 13 480 1.0× 177 0.8× 36 0.7× 15 0.5× 18 0.7× 19 534
Ahmet Beycioğlu Türkiye 15 528 1.1× 359 1.5× 51 1.0× 101 3.3× 18 0.7× 39 688
Reza Imaninasab Iran 15 542 1.1× 147 0.6× 59 1.2× 20 0.6× 27 1.0× 25 642
Huaguo Chen Hong Kong 12 260 0.5× 198 0.8× 23 0.5× 30 1.0× 18 0.7× 21 372

Countries citing papers authored by Hai‐Van Thi

Since Specialization
Citations

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

Fields of papers citing papers by Hai‐Van Thi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hai‐Van Thi. 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 Hai‐Van Thi. The network helps show where Hai‐Van Thi may publish in the future.

Co-authorship network of co-authors of Hai‐Van Thi

This figure shows the co-authorship network connecting the top 25 collaborators of Hai‐Van Thi. A scholar is included among the top collaborators of Hai‐Van Thi 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 Hai‐Van Thi. Hai‐Van Thi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
1.
Thi, Hai‐Van, et al.. (2024). Application of extreme gradient boosting in predicting the viscoelastic characteristics of graphene oxide modified asphalt at medium and high temperatures. Frontiers of Structural and Civil Engineering. 18(6). 899–917. 1 indexed citations
2.
Thi, Hai‐Van, Son Hoang Trinh, & Hai‐Bang Ly. (2023). Enhancing Compressive strength prediction of Roller Compacted concrete using Machine learning techniques. Measurement. 218. 113196–113196. 15 indexed citations
3.
Thi, Hai‐Van, May Huu Nguyen, & Hai‐Bang Ly. (2023). Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis. Construction and Building Materials. 367. 130339–130339. 76 indexed citations
4.
Thi, Hai‐Van, May Huu Nguyen, Son Hoang Trinh, & Hai‐Bang Ly. (2023). Toward improved prediction of recycled brick aggregate concrete compressive strength by designing ensemble machine learning models. Construction and Building Materials. 369. 130613–130613. 53 indexed citations
5.
Thi, Hai‐Van, May Huu Nguyen, Son Hoang Trinh, & Hai‐Bang Ly. (2023). Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete. Frontiers of Structural and Civil Engineering. 17(2). 284–305. 19 indexed citations
6.
7.
Le, Thanh-Hai, et al.. (2023). Advancing basalt fiber asphalt concrete design: A novel approach using gradient boosting and metaheuristic algorithms. Case Studies in Construction Materials. 19. e02528–e02528. 10 indexed citations
8.
Tran, Van Quan, et al.. (2022). Machine learning approach in investigating carbonation depth of concrete containing Fly ash. Structural Concrete. 24(2). 2145–2169. 38 indexed citations
9.
Nguyen, May Huu, Hai‐Van Thi, Son Hoang Trinh, & Hai‐Bang Ly. (2022). A comparative assessment of tree-based predictive models to estimate geopolymer concrete compressive strength. Neural Computing and Applications. 35(9). 6569–6588. 32 indexed citations
10.
Tran, Van Quan, Hai‐Van Thi, Thuy‐Anh Nguyen, & Hai‐Bang Ly. (2022). Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete. Frontiers of Structural and Civil Engineering. 16(7). 928–945. 14 indexed citations
11.
Thi, Hai‐Van, Thuy‐Anh Nguyen, Hai‐Bang Ly, & Van Quan Tran. (2021). Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast‐Furnace Slag and Fly Ash. Advances in Materials Science and Engineering. 2021(1). 42 indexed citations
12.
Nguyen, Thuy‐Anh, Hai‐Bang Ly, Hai‐Van Thi, & Van Quan Tran. (2021). Using ANN to Estimate the Critical Buckling Load of Y Shaped Cross-Section Steel Columns. Scientific Programming. 2021. 1–8. 13 indexed citations
13.
Thi, Hai‐Van, Thuy‐Anh Nguyen, Hai‐Bang Ly, & Van Quan Tran. (2021). Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model. Advances in Civil Engineering. 2021(1). 67 indexed citations
14.
Nguyen, Thuy‐Anh, Hai‐Bang Ly, Hai‐Van Thi, & Van Quan Tran. (2021). On the Training Algorithms for Artificial Neural Network in Predicting the Shear Strength of Deep Beams. Complexity. 2021(1). 29 indexed citations
15.
Tran, Van Quan, Hai‐Van Thi, Thuy‐Anh Nguyen, & Hai‐Bang Ly. (2021). Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS. PLoS ONE. 16(12). e0260847–e0260847. 13 indexed citations
16.
Ly, Hai‐Bang, Thuy‐Anh Nguyen, Hai‐Van Thi, & Van Quan Tran. (2021). Development of deep neural network model to predict the compressive strength of rubber concrete. Construction and Building Materials. 301. 124081–124081. 143 indexed citations
17.
Nguyen, Thuy‐Anh, Hai‐Bang Ly, Hai‐Van Thi, & Van Quan Tran. (2020). Prediction of Later‐Age Concrete Compressive Strength Using Feedforward Neural Network. Advances in Materials Science and Engineering. 2020(1). 21 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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