• Assistant researcher
• Name (Simplified Chinese):王赶
• Name (Pinyin):wanggan
• Date of Birth:1996-06-24
• E-Mail:2943821998ad3489721162423fd0399e18352f732e6ed8abb2d69cf7b9eec723db395aa5cabd0eefaee5d06c3acbc5667b14274adeefe2e4bee31d88c78d4255118c605e84bc8bcd243a10aa3c7332f4dba20dae69a3a5e64edd358197cb0886823994065ef7494bccdb7b925f0af258a546fb6a068ae8d26a68f66801ad6d63
• Date of Employment:2025-07-14
• School/Department:道桥与地下工程系
• Administrative Position:助理研究员
• Education Level:With Certificate of Graduation for Doctorate Study
• Business Address:土木楼416
• Gender:Male
• Contact Information:gwang@hfut.edu.cn
• Degree:Doctoral degree
• Status:Employed
• Alma Mater:北京交通大学
• Discipline:Bridge and Tunnel Engineering
  

• ZipCode:23ea49f7ae69ee1a055fdd07803771e825ffcf3fddeb22b2078c89992b812c12898d531b95c5bee0497a75eaf27ab4e752e089bdc1ce39996c360c96c607ae6f2ac5af5f151a998a8febbb9a771558837a8b4a98342207771e2b6019517f3021628f45b2c3767ccf10f652a225a1b700b15de192a1f79659fbc81859ff5608ce
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王赶，博士，合肥工业大学。研究方向主要包括盾构隧道智能建造、隧道服役期性能，盾构施工地层扰动分析。

先后在国际隧道工程领域的知名期刊，如《Computer-Aided Civil and Infrastructure Engineering》、《International Journal of Rock Mechanics and Mining Sciences》、《Automation in Construction》和《Tunnelling and Underground Space Technology》等国内外重要期刊发表了论文30篇，其中SCI检索的期刊论文23篇（4篇ESI高被引论文和1篇ESI热点论文），h-index指数9。一作SCI论文10篇，其中，中科院1区TOP论文8篇。主持研究生创新项目2项，参与国家自然科学基金重点项目2项，其它纵向课题3项，横向课题10项。作为第1发明人申请发明专利3项，其它专利8项。参编著作与规范各1部。为响应国家数字化与智能化的方针策略，参与了盾构隧道智能建造平台的开发，负责系统后台的参数预测与决策算法研发与API接口搭建。

微信号：gan_wang_hfut

邮箱：gwang@hfut.edu.cn

英文主页：https://www.researchgate.net

OCRID:  https://orcid.org/0000-0002-2408-948X

主要研究方向：

1 盾构隧道智能建造方向

[1]   Wang, G., Fang, Q., Du, J., Wang, J., Li, Q., 2023. Deep learning-based prediction of steady surface settlement due to shield tunnelling. Automation in Construction 154, 105006. (中科院1区 Top, IF 10.3，高被引论文)

[2]   Wang, G., Fang, Q., Wang, J., Li, Q., Song, H., Huang, J., 2025. Artificial intelligence prediction of surface settlement induced by twin shields tunnelling. Tunnelling Underground Space Technol. 161, 106606. (中科院1区 Top, IF 6.9)

[3]   Wang, G., Fang, Q., Wang, J., Li, 2025. Knowledge-based intelligence method for controlling segment floating by optimizing shield tail grouting parameters. Smart Construction.

2 隧道服役期性能方向

[1]   Wang, G., Fang, Q., Du, J., Wang, J., 2023. Semi-analytical solution for internal forces of tunnel lining with multiple longitudinal cracks. Journal of Rock Mechanics and Geotechnical Engineering 15, 2013–2024. (中科院1区 Top, IF 9.6)

[2]   Wang, G., Fang, Q., Du, J., Yang, X., Wang, J., 2022. Estimating Volume Loss for Shield-Driven Tunnels Based on the Principle of Minimum Total Potential Energy. Applied Sciences 12, 1794.

[3]   Wang, G., Fang, Q., Wang, J., Li, Q.M., Chen, J.Y., Liu, Y., 2024. Estimation of load for tunnel lining in elastic soil using physics‐informed neural network. Comput.-aided Civ. Infrastruct. Eng. mice.13208. (中科院1区Top, IF 9.6，高被引论文)

3 隧道施工地层扰动分析方向

[1]   Fang, Q., Wang, G., Yu, F., Du, J., 2021. Analytical algorithm for longitudinal deformation profile of a deep tunnel. J. Rock Mech. Geotech. Eng. 13, 845–854. (中科院1区 Top, IF 9.6，高被引、热点论文)

[2]   Fang Q., Wang G., Du J., Liu Y., Zhou M., 2023. Prediction of tunnelling induced ground movement in clay using principle of minimum total potential energy. Tunnelling Underground Space Technol. 131, 104854. (中科院1区 Top, IF 6.9，高被引论文)

[3]   Wang, G., Fang, Q., Wang, J., Li, Q.M., 2025. Ground movement prediction for twin tunnels with minimum total potential energy principle considering volumetric change of soil. Transportation Geotechnics. (中科院2区, IF 4.9)