Maolin Shi

1.6k total citations · 1 hit paper
52 papers, 1.2k citations indexed

About

Maolin Shi is a scholar working on Mechanical Engineering, Civil and Structural Engineering and Ocean Engineering. According to data from OpenAlex, Maolin Shi has authored 52 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Mechanical Engineering, 21 papers in Civil and Structural Engineering and 14 papers in Ocean Engineering. Recurrent topics in Maolin Shi's work include Tunneling and Rock Mechanics (16 papers), Drilling and Well Engineering (12 papers) and Mineral Processing and Grinding (9 papers). Maolin Shi is often cited by papers focused on Tunneling and Rock Mechanics (16 papers), Drilling and Well Engineering (12 papers) and Mineral Processing and Grinding (9 papers). Maolin Shi collaborates with scholars based in China, Australia and Canada. Maolin Shi's co-authors include Xueguan Song, Chao Zhang, Sun We, Hongwei Zhang, Junhong Zhao, Liye Lv, Jian Zhang, Liyong Zhang, Lizhang Xu and Muxi Li and has published in prestigious journals such as Analytical Chemistry, Small and IEEE Access.

In The Last Decade

Maolin Shi

49 papers receiving 1.2k citations

Hit Papers

Recurrent neural networks for real-time prediction of TBM... 2018 2026 2020 2023 2018 50 100 150 200
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. Recurrent neural networks for real-time prediction of TBM operating parameters
    2018 239 citations Maolin Shi, Xueguan Song et al. Automation in Construction profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maolin Shi China 17 695 427 324 256 111 52 1.2k
M.R. Haddara Canada 20 500 0.7× 436 1.0× 377 1.2× 267 1.0× 144 1.3× 80 1.3k
Yunlai Zhou China 20 956 1.4× 123 0.3× 484 1.5× 436 1.7× 17 0.2× 104 1.4k
Geoff Rideout Canada 15 419 0.6× 387 0.9× 421 1.3× 117 0.5× 24 0.2× 65 1.0k
Beom-Seon Jang South Korea 18 246 0.4× 185 0.4× 386 1.2× 241 0.9× 55 0.5× 95 927
Joaquím Blesa Spain 26 869 1.3× 236 0.6× 162 0.5× 55 0.2× 47 0.4× 110 1.8k
Lei Si China 19 202 0.3× 234 0.5× 530 1.6× 344 1.3× 116 1.0× 90 1.3k
Wei Liang China 13 287 0.4× 154 0.4× 266 0.8× 170 0.7× 86 0.8× 63 775

Countries citing papers authored by Maolin Shi

Since Specialization
Citations

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

Fields of papers citing papers by Maolin Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maolin Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Maolin Shi. A scholar is included among the top collaborators of Maolin Shi 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 Maolin Shi. Maolin Shi 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.
2.
Sun, Qingchao, et al.. (2024). A novel dual-channel deep neural network for tunnel boring machine slurry circulation system data prediction. Advances in Engineering Software. 201. 103853–103853. 2 indexed citations
3.
Li, Yang, et al.. (2024). Modeling and control methods of a multi-parameter system for threshing and cleaning in grain combine harvesters. Computers and Electronics in Agriculture. 225. 109251–109251. 13 indexed citations
4.
5.
Wang, Hao, Lixue Liu, Maolin Shi, et al.. (2023). Active learning framework for tunnel geological reconstruction based on TBM operational data. Automation in Construction. 158. 105230–105230. 13 indexed citations
6.
Kolahdooz, Amin, et al.. (2023). Robust topology optimization of continuum structures with smooth boundaries using moving morphable components. Structural and Multidisciplinary Optimization. 66(6). 14 indexed citations
7.
Shi, Maolin & Zihao Wang. (2022). A Study of Support Vector Regression-Based Fuzzy c-Means Algorithm on Incomplete Data Clustering. Journal of Advanced Computational Intelligence and Intelligent Informatics. 26(4). 483–494.
8.
Shi, Maolin, Weifei Hu, Muxi Li, et al.. (2022). Ensemble regression based on polynomial regression-based decision tree and its application in the in-situ data of tunnel boring machine. Mechanical Systems and Signal Processing. 188. 110022–110022. 102 indexed citations
9.
Chen, Shuren, et al.. (2022). Field Investigation of the Static Friction Characteristics of High-Yielding Rice during Harvest. Agriculture. 12(3). 327–327. 9 indexed citations
10.
Shi, Maolin, Zihao Wang, & Lizhang Xu. (2021). A fuzzy clustering algorithm based on hybrid surrogate model. Journal of Intelligent & Fuzzy Systems. 42(3). 1963–1976. 1 indexed citations
11.
Zhang, Chao, Xueguan Song, Lixue Liu, et al.. (2021). Generative adversarial network for geological prediction based on TBM operational data. Mechanical Systems and Signal Processing. 162. 108035–108035. 48 indexed citations
12.
Song, Xueguan, et al.. (2020). Application of FCMAlgorithm Combined with Artcial Neural Network in TBM Operation Data. Computer Modeling in Engineering & Sciences. 126(1). 397–417. 7 indexed citations
13.
Shi, Maolin, Wei Sun, Xueguan Song, & Hongyou Li. (2019). High-low level support vector regression prediction approach (HL-SVR) for data modeling with input parameters of unequal sample sizes. arXiv (Cornell University). 4 indexed citations
14.
Li, Hongyou, et al.. (2018). Uncertainty optimization of dental implant based on finite element method, global sensitivity analysis and support vector regression. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 233(2). 232–243. 14 indexed citations
15.
We, Sun, Xiaobang Wang, Maolin Shi, Zhuqing Wang, & Xueguan Song. (2018). Multidisciplinary design optimization of hard rock tunnel boring machine using collaborative optimization. Advances in Mechanical Engineering. 10(1). 20 indexed citations
16.
Zhang, Xinhua, et al.. (2017). Technical and economic analysis on grid-connected wind farm based on hybrid energy storage system and distributed generators. AIP conference proceedings. 1839. 20051–20051. 1 indexed citations
17.
Shi, Maolin & Hongyou Li. (2016). The effect of complexing agent on Ti alloy micro-arc oxidation(MAO) coatings in Ca-P electrolyte. Protection of Metals and Physical Chemistry of Surfaces. 52(5). 900–909. 4 indexed citations
18.
Shi, Maolin, Jing Xu, Yawei Dai, et al.. (2016). Plasma enhanced atomic layer deposition of molybdenum oxide from Mo(CO)6 and O2 plasma for 2D electronic device application. 21. 1014–1016. 1 indexed citations
19.
Shi, Maolin, et al.. (2015). Investigation of self-heating effect in SOI tunnel field-effect transistor. 1–4. 4 indexed citations
20.
Shi, Maolin & Hongyou Li. (2015). A Mathematical interpretation model of Ti alloy micro-arc oxidation (MAO) process and its experimental study. Surface Engineering and Applied Electrochemistry. 51(5). 468–477. 6 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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