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)