職稱：博士后

郵箱：whu_meijz@whu.edu.cn

研究方向：

巖土顆粒材料宏細(xì)觀力學(xué)特性、庫壩系統(tǒng)安全

教育背景：

2014.09-2018.06 武漢大學(xué)水利水電學(xué)院 水利水電工程，本科

2018.09-2023.12 武漢大學(xué)水利水電學(xué)院 水工結(jié)構(gòu)工程，博士

工作經(jīng)歷：

2024.01-2024.05 武漢大學(xué)水利水電學(xué)院，科研助理

2024.06-至今 武漢大學(xué)水利水電學(xué)院，博士后

代表性科研項目：

國家重點(diǎn)研發(fā)計劃項目課題，中小流域堤壩群復(fù)雜險情防控和應(yīng)急處置技術(shù)裝備與材料，2022.11-2025.10，參與

國家自然科學(xué)基金面上項目，基于多源監(jiān)測數(shù)據(jù)和智能算法的堆石壩變形預(yù)測，2022.01-2025.12，參與

獎勵與榮譽(yù)：

第七屆全國顆粒材料計算力學(xué)會議優(yōu)秀報告獎（2024）

武漢大學(xué)研究生學(xué)術(shù)創(chuàng)新獎二等獎（2023）

武漢大學(xué)長江電力獎學(xué)金（2022）

代表性學(xué)術(shù)成果：

（*表示通訊作者）

1.Mei, J., Ma, G.*, Liu, J., Nicot, F.*, & Zhou, W. (2023). Modeling shear-induced solid-liquid transition of granular materials using persistent homology. Journal of the Mechanics and Physics of Solids, 176, 105307. https://doi.org/10.1016/j.jmps.2023.105307

2.Ma, G., Mei, J.*, Gao, K., Zhao, J., Zhou, W., & Wang, D. (2022). Machine learning bridges microslips and slip avalanches of sheared granular gouges. Earth and Planetary Science Letters, 579, 117366. https://doi.org/10.1016/j.epsl.2022.117366

3.Mei, J., Ma, G.*, Tang, L., Gao, K., Cao, W., & Zhou, W. (2023). Spatial clustering of microscopic dynamics governs the slip avalanche of sheared granular materials. International Journal of Plasticity, 163, 103570. https://doi.org/10.1016/j.ijplas.2023.103570

4.Mei, J., Ma, G.*, Wang, Q., Wu, T., & Zhou, W. (2022). Micro- and macroscopic aspects of the intermittent behaviors of granular materials related by graph neural network. International Journal of Solids and Structures, 251, 111763. https://doi.org/10.1016/j.ijsolstr.2022.111763

5.梅江洲, 馬剛*, 鄒宇雄, 王頔, 周偉, 常曉林. (2022). 顆粒斷層泥黏滑運(yùn)動的研究進(jìn)展. 中國科學(xué):技術(shù)科學(xué), 52(07):984-998.

6.Cao, W., Mei, J., Yang, X., Zhou, W., Chang, X., & Ma, G.* (2024). A network-based investigation on static liquefaction of sheared granular materials. Granular Matter, 26(3). https://doi.org/10.1007/s10035-024-01433-3

7.Zou, Y., Ma, G.*, Mei, J., Zhao, J., & Zhou, W. (2022). Microscopic origin of shape-dependent shear strength of granular materials: a granular dynamics perspective. Acta Geotechnica, 17(7), 2697–2710. https://doi.org/10.1007/s11440-021-01403-6

8.Wang, Y., Ma, G.*, Mei, J., Zou, Y., Zhang, D., Zhou, W., & Cao, X. (2021). Machine learning reveals the influences of grain morphology on grain crushing strength. Acta Geotechnica, 16(11), 3617–3630. https://doi.org/10.1007/s11440-021-01270-1

9.Zhang, J., Ma, G.*, Yang, Z., Mei, J., Zhang, D., Zhou, W., & Chang, X. (2024). Knowledge Extraction via Machine Learning Guides a Topology‐Based Permeability Prediction Model. Water Resources Research, 60(7). https://doi.org/10.1029/2024WR037124