Bokai Liu

1.3k total citations
16 papers, 912 citations indexed

About

Bokai Liu is a scholar working on Materials Chemistry, Mechanics of Materials and Civil and Structural Engineering. According to data from OpenAlex, Bokai Liu has authored 16 papers receiving a total of 912 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Materials Chemistry, 7 papers in Mechanics of Materials and 4 papers in Civil and Structural Engineering. Recurrent topics in Bokai Liu's work include Composite Material Mechanics (6 papers), Thermal properties of materials (5 papers) and Machine Learning in Materials Science (4 papers). Bokai Liu is often cited by papers focused on Composite Material Mechanics (6 papers), Thermal properties of materials (5 papers) and Machine Learning in Materials Science (4 papers). Bokai Liu collaborates with scholars based in Sweden, Germany and China. Bokai Liu's co-authors include Timon Rabczuk, N. Vu‐Bac, Weizhuo Lu, Xiaoying Zhuang, Xiaolong Fu, Thomas Olofsson, Yizheng Wang, Kailun Feng, Chao Zhang and Hongyuan Fang and has published in prestigious journals such as Renewable Energy, Composites Science and Technology and Composite Structures.

In The Last Decade

Bokai Liu

16 papers receiving 883 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Bokai Liu Sweden	12	273	239	208	195	91	16	912
Hua Zhou China	15	200 0.7×	179 0.7×	131 0.6×	86 0.4×	58 0.6×	56	904
Thang Nguyen Japan	15	210 0.8×	140 0.6×	424 2.0×	312 1.6×	119 1.3×	73	1.1k
Yu. I. Dimitrienko Russia	16	223 0.8×	381 1.6×	248 1.2×	135 0.7×	64 0.7×	107	894
Hongxin Wang China	17	121 0.4×	194 0.8×	239 1.1×	288 1.5×	97 1.1×	51	824
Sang-Ho Lee South Korea	19	207 0.8×	122 0.5×	141 0.7×	388 2.0×	127 1.4×	63	897
D. Saifaoui Morocco	16	91 0.3×	202 0.8×	134 0.6×	74 0.4×	26 0.3×	73	720
S. Joseph Antony United Kingdom	20	219 0.8×	321 1.3×	274 1.3×	523 2.7×	26 0.3×	74	1.5k
Long Liu China	16	154 0.6×	239 1.0×	154 0.7×	182 0.9×	74 0.8×	87	696
Zhu Li China	14	94 0.3×	335 1.4×	103 0.5×	140 0.7×	27 0.3×	80	671

Countries citing papers authored by Bokai Liu

Since Specialization

Citations

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

Fields of papers citing papers by Bokai Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bokai Liu

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

All Works

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16 of 16 papers shown

1.

Liu, Bokai, Pengju Liu, Yizheng Wang, et al.. (2025). Explainable machine learning for multiscale thermal conductivity modeling in polymer nanocomposites with uncertainty quantification. Composite Structures. 370. 119292–119292. 16 indexed citations

2.

Zhang, Xi, Yangyang Xia, Chao Zhang, et al.. (2025). A prediction model for dyke-dam piping based on data augmentation and interpretable ensemble learning. Engineering Failure Analysis. 182. 110174–110174. 1 indexed citations

3.

Zhang, Xi, Yangyang Xia, Chao Zhang, et al.. (2025). An Archimedes Optimization Algorithm Based Extreme Gradient Boosting Model for Predicting the Bending Strength of UV Cured Glass Fiber Reinforced Polymer Composites. Polymer Composites. 47(5). 4228–4245. 2 indexed citations

4.

Liu, Bokai, Pengju Liu, Weizhuo Lu, & Thomas Olofsson. (2025). Explainable Artificial Intelligence (XAI) for Material Design and Engineering Applications: A Quantitative Computational Framework. DiVA at Umeå University (Umeå University). 5(2). 236–265. 13 indexed citations

5.

Liu, Bokai, Weizhuo Lu, Chengqi Zhang, et al.. (2023). Multiscale Modeling of Thermal Properties in Polyurethane Incorporated With Phase Change Materials Composites: A Case Study. SSRN Electronic Journal. 1 indexed citations

6.

Liu, Bokai, et al.. (2023). Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden. Technology in Society. 75. 102347–102347. 54 indexed citations

7.

Liu, Bokai, Weizhuo Lu, Thomas Olofsson, Xiaoying Zhuang, & Timon Rabczuk. (2023). Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites. Composite Structures. 327. 117601–117601. 81 indexed citations

8.

Xia, Yangyang, Chao Zhang, Cuixia Wang, et al.. (2023). Prediction of bending strength of glass fiber reinforced methacrylate-based pipeline UV-CIPP rehabilitation materials based on machine learning. Tunnelling and Underground Space Technology. 140. 105319–105319. 42 indexed citations

9.

Liu, Bokai, Yizheng Wang, Timon Rabczuk, Thomas Olofsson, & Weizhuo Lu. (2023). Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks. Renewable Energy. 220. 119565–119565. 86 indexed citations

10.

Liu, Bokai & Weizhuo Lu. (2022). Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design. International Journal of Hydromechatronics. 1(1). 1–1. 6 indexed citations

11.

Liu, Bokai, N. Vu‐Bac, Xiaoying Zhuang, Xiaolong Fu, & Timon Rabczuk. (2022). Stochastic full-range multiscale modeling of thermal conductivity of Polymeric carbon nanotubes composites: A machine learning approach. Composite Structures. 289. 115393–115393. 106 indexed citations

12.

Liu, Bokai & Weizhuo Lu. (2022). Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design. International Journal of Hydromechatronics. 5(4). 336–336. 93 indexed citations

13.

Liu, Bokai, N. Vu‐Bac, Xiaoying Zhuang, et al.. (2022). Al-DeMat: A web-based expert system platform for computationally expensive models in materials design. Advances in Engineering Software. 176. 103398–103398. 58 indexed citations

14.

Liu, Bokai, N. Vu‐Bac, Xiaoying Zhuang, Xiaolong Fu, & Timon Rabczuk. (2022). Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites. Composites Science and Technology. 224. 109425–109425. 130 indexed citations

15.

Liu, Bokai, N. Vu‐Bac, & Timon Rabczuk. (2021). A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms. Composite Structures. 273. 114269–114269. 131 indexed citations

16.

Liu, Bokai, N. Vu‐Bac, Xiaoying Zhuang, & Timon Rabczuk. (2019). Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites. Mechanics of Materials. 142. 103280–103280. 92 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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