孙震，教授，博士生导师。本科毕业于山东大学，硕士毕业于同济大学，师从中国科学院院士李杰教授，博士毕业于日本东京大学/横滨国立大学，师从Yozo Fujino教授。在葡萄牙波尔图大学完成博士后研究，师从Elsa Caetano教授。主要从事结构健康监测、机器学习和数据驱动的桥梁性能预测、缆索承重桥梁损伤机制与安全预警、基于车桥耦合作用的桥梁损伤识别及评估等研究。

入选国家高层次青年人才项目、江苏省交通运输行业高层次领军人才培养计划（第二层次）、江苏省科协“青年科技人才托举工程”。担任中国建设监理协会工程监测与诊治分会常务理事、中国振动工程学会结构抗振控制与健康监测青年委员会委员。担任SCI期刊Engineering Structures （影响因子5.6）、Structural Control and Health Monitoring （影响因子4.6） 、Engineering Failure Analysis（影响因子4）特刊编辑。

每年招收硕士生2名，博士生2名。与美国、日本、葡萄牙、挪威、荷兰、德国、意大利等多个国家的知名学者团队有良好的合作关系，可为学生提供赴海外交流学习的机会。

SCI期刊Engineering Structures （影响因子5.6）、Structural Control and Health Monitoring （影响因子4.6） 、Engineering Failure Analysis（影响因子4）特刊编辑

期刊Data In Brief （ESCI，影响因子1.2）编委

中国建设监理协会工程监测与诊治分会常务理事

中国振动工程学会结构抗振控制与健康监测青年委员会委员

30多个SCI期刊的审稿人

本科生课程：

《结构有限元分析与软件应用》

科技部中国-斯洛伐克合作交流项目,负责人

国家重点研发计划,课题和子课题各1项，负责人

葡萄牙自然科学基金（FCT）项目，分项负责人

英国工程与自然科学研究委员会(EPSRC)项目，技术骨干

江苏省交通科技项目，项目负责人

福建省交通科技项目，技术负责人

江苏交通控股有限公司科研项目，项目负责人

近三年期刊论文

(1) Liu, Z., Tan, S., Jia, D., Sun, C., Jiang, Z., Guo, T., & Sun, Z.* (2025). Exploring scour mechanisms of bridge group under tidal Conditions: In-situ survey, laboratory experiment, and numerical analysis. Ocean Engineering, 338, 122025. 

(2) Zhang, Z., Fu, Y., & Sun, Z.* (2025). Advancements in digital twin-enhanced health monitoring and condition assessment of cable-supported bridges. Structures, Vol. 79, 109448. 

(3) Lei, X., Sun, Z.*, Wang, A., Guo, T., & Nagayama, T. (2025). Estimation of bridge girder cumulative displacement for component operational warning using Bayesian neural networks. Structural Control and Health Monitoring, 2025(1), 9974584. 

(4) Sun, Z., Xie, T., Li, M., & Guo, T. (2025). Quasi-real-time prediction and warning of stay cable vibration subjected to typhoon with a Nonlinear Autoregressive Neural Network and KNN. Engineering Structures, 340, 120661. 

(5) Lei, X., Sun, M., Sun, Z.*, Siringoringo, D. M., & Dong, Y. (2025). Data-based feature representation of traffic flow for predicting bridge displacement responses with ensemble learning model. Journal of Civil Structural Health Monitoring, 15(4), 1081-1099. 

(6) Ding, Z., Liu, H., Demartino, C., Feng, M., & Sun, Z.* (2024). Neighborhood component analysis-based feature selection in machine learning to predict tendon ultimate stress of unbonded prestressed concrete beams. Case Studies in Construction Materials, 21, e03428. 

(7) Sun, Z., Ye, X. W., & Lu, J. (2024). Estimating stay cable vibration under typhoon with an explainable ensemble learning model. Structure and Infrastructure Engineering, 20(12), 1912-1924. 

(8) Sun, Z., Siringoringo, D. M., Chen, S. Z., & Lu, J. (2023). Cumulative displacement-based detection of damper malfunction in bridges using data-driven isolation forest algorithm. Engineering Failure Analysis, 143, 106849. 

(9) Zhong, Q. M., Chen, S. Z.*, Sun, Z.*, & Tian, L. C. (2023). Fully automatic operational modal analysis method based on statistical rule enhanced adaptive clustering method. Engineering Structures, 274, 115216. 

(10) Sun, Z., Santos, J., Caetano, E., & Oliveira, C. (2023). Interpreting cumulative displacement in a suspension bridge with a physics-based characterisation of environment and roadway/railway loads. Journal of Civil Structural Health Monitoring, 13(2), 387-397. 

(11) Ye, X. W., Sun, Z.*, & Lu, J. (2023). Prediction and early warning of wind-induced girder and tower vibration in cable-stayed bridges with machine learning-based approach. Engineering Structures, 275, 115261. 

(12) Sun, H., Sun, Z.*, & Yao, Y. (2023). Hanger replacement and corrosion assessment in a suspension bridge. Structures, Vol. 58, 105501.

(13) Sun, Z., Caetano, E., Pereira, S., & Moutinho, C. (2023). Employing histogram of oriented gradient to enhance concrete crack detection performance with classification algorithm and Bayesian optimization. Engineering Failure Analysis, 150, 107351. 

(14) Sun, Z., Sun, M., Siringoringo, D. M., Dong, Y., & Lei, X. (2023). Predicting bridge longitudinal displacement from monitored operational loads with hierarchical CNN for condition assessment. Mechanical Systems and Signal Processing, 200, 110623. 

(15) Lei, X., Siringoringo, D. M., Dong, Y., & Sun, Z.* (2023). Interpretable machine learning methods for clarification of load-displacement effects on cable-stayed bridge. Measurement, 220, 113390. 

(16) Feng, D. C., Wang, W. J., Mangalathu, S., & Sun, Z.* (2023). Condition Assessment of Highway Bridges Using Textual Data and Natural Language Processing‐(NLP‐) Based Machine Learning Models. Structural Control and Health Monitoring, 2023(1), 9761154. 

(17) Lei, X., Siringoringo, D. M., Sun, Z.*, & Fujino, Y. (2023). Displacement response estimation of a cable-stayed bridge subjected to various loading conditions with one-dimensional residual convolutional autoencoder method. Structural Health Monitoring, 22(3), 1790-1806. 

(18) Sun, Z., & Ye, X. W. (2022). Incorporating site-specific weigh-in-motion data into fatigue life assessment of expansion joints under dynamic vehicle load. Engineering Structures, 255, 113941. 

(19) Sun, Z., Santos, J., & Caetano, E. (2022). Vision and support vector machine–based train classification using weigh-in-motion data. Journal of Bridge Engineering, 27(6), 06022001. 

(20) Sun, Z., Santos, J., & Caetano, E. (2022). Data‐driven prediction and interpretation of fatigue damage in a road‐rail suspension bridge considering multiple loads. Structural Control and Health Monitoring, 29(9), e2997. 

(21) Sun, Z., Feng, D. C., Mangalathu, S., Wang, W. J., & Su, D. (2022). Effectiveness assessment of TMDs in bridges under strong winds incorporating machine-learning techniques. Journal of Performance of Constructed Facilities, 36(5), 04022036. (Citation: 27)