[1] 国家自然科学基金青年基金（C类）：水下盾构隧道衬砌结构防水性能劣化时空预测及渗漏水智能预警研究，2026-01至2028-12，30万，在研,主持

[2] 湖北省科技创新人才项目：城市盾构隧道时空多尺度力学行为智能计算与预测预警研究，2025-08至2027-08，30万，在研,主持

[3] 国家重点研发项目子课题：地质储氢库适用性及安全关键技术，2024-12至2027-11，100万，在研,主持

[4] 中国科学院特别研究助理资助项目：水下盾构隧道健康监测大数据驱动的结构力学行为研究，2022-01至2023-12，60万，结题,主持

[5] 中国科学院重点部署项目子课题：不良地质地下结构监测数据增强及异常性态辨识，2021-03至2024-3，100万，结题,主持

[1] Tan Xuyan, Palaiahnakote Shivakumara, Chen Weizhong, et al. A novel autoencoder for structural anomalies detection in river tunnel operation, Expert Systems with Applications, 2024, 244(2):122906.

[2] Tan Xuyan, Chen Weizhong, Tan Xianjun, et al. Prediction for the future mechanical behavior of underwater shield tunnel fusing deep learning algorithm on SHM data, Tunnelling and Underground Space Technology, 2022, 125: 104504.

[3] Tan Xuyan, Chen Weizhong, Zou Tao, et al. Real-time prediction of mechanical behaviors of underwater shield tunnel structure using machine learning method based on structural health monitoring data, Journal of Rock Mechanics and Geotechnical Engineering, 2022,15(4): 886-895.

[4] Tan Xuyan, Chen Weizhong, Wu Guojun, et al. A structural health monitoring system for data analysis of segment joint opening in an underwater shield tunnel, Structural Health Monitoring, 2019, 19(6): 1032-1050.

[5] Tan Xuyan, Chen Weizhong, Yang Jianping, et al. Prediction for segment strain and opening of underwater shield tunnel through deep learning method, Transportation Geotechnics, 2023, 39: 100928.

[6] Tan Xuyan, Sun Xuanxuan, Chen Weizhong, et al. Investigation on the data augmentation using machine learning algorithms in structural health monitoring information. Structural Health Monitoring, 2021, 20(4): 2054-2068.

[7] Tan Xuyan, Wang Yuhang, Du Bowen, et al. Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data. Structural Health Monitoring, 2022, 21(4): 1805-1818.

[8] Tan Xuyan, Chen Weizhong, Gao Hou, et al. Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data. Structural Health Monitoring, 2023, 23(1): 175-186.

[9] Tan Xuyan, Tan Xianjun, Zhang Rui, et al. Identification of anomaly of tunnel segment strain using an adaptive machine learning model. Georisk, 2024, 18(4): 765-778

[10] Tan Xuyan, Chen Weizhong, Qin Changkun, et al. Characterisation for spatial distribution of mining induced stress through deep learning algorithm on SHM data. Georisk, 2023, 17(1): 217-226.

[11] Tan Xuyan, Chen Weizhong, Yang Jianping, et al. Temporal-spatial coupled model for multi-prediction of tunnel structure: using deep attention-based temporal convolutional network. Journal of Civil Structural Health Monitoring, 2022, 12: 675-687.

[14] Tan Xuyan, Chen Weizhong, Tan Xianjun, et al. Missing data imputation in tunnel monitoring with a spatio-temporal correlation fused machine learning model. Journal of Civil Structural Health Monitoring, 2024.

[12] Tan Xuyan, Chen Weizhong, Fan Lixiang, et al. Spatial Dynamic Early Warning of Different Positions in Underwater Tunnel Driven by Real‐Time Monitoring Data. Structural Control & Health Monitoring. 2025.

[13] Tan Xuyan, Chen Weizhong, Yang Jianping, et al. Application of a data-driven intelligent information system in infrastructure: underwater tunnel case study. ASCE-Journal of Performance of Constructed Facilities, 2022, 37(1).

[15] 谭旭燕，陈卫忠，杜博文，等. 数据驱动的水下盾构隧道衬砌应变行为预测研究，岩石力学与工程学报. 2022 ,41: 3317-3326.

中国岩石力学与工程学会科技一等奖（排3）

中国岩石力学与工程学会优秀博士学位论文奖