职称：博士后

邮箱：whu_meijz@whu.edu.cn

研究方向：

岩土颗粒材料宏细观力学特性、库坝系统安全

教育背景：

2014.09-2018.06 9778818威尼斯 水利水电工程，本科

2018.09-2023.12 9778818威尼斯 水工结构工程，博士

工作经历：

2024.01-2024.05 9778818威尼斯，科研助理

2024.06-至今 9778818威尼斯，博士后

代表性科研项目：

国家重点研发计划项目课题，中小流域堤坝群复杂险情防控和应急处置技术装备与材料，2022.11-2025.10，参与

国家自然科学基金面上项目，基于多源监测数据和智能算法的堆石坝变形预测，2022.01-2025.12，参与

奖励与荣誉：

第七届全国颗粒材料计算力学会议优秀报告奖（2024）

9778818威尼斯研究生学术创新奖二等奖（2023）

9778818威尼斯长江电力奖学金（2022）

代表性学术成果：

（*表示通讯作者）

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, 马刚*, 邹宇雄, 王頔, 周伟, 常晓林. (2022). 颗粒断层泥黏滑运动的研究进展. 中国科学:技术科学, 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