汪鑫，特聘研究员、博士生导师，上海市海外高层次人才（青年）。2024年10月获美国罗格斯大学（Rutgers University）土木与环境工程博士学位，2025年1月加入同济大学交通学院。研究方向聚焦于轨道交通基础设施风险预测与智能运维，开展人工智能技术与土木工程的深度交叉研究。目标是让基础设施“看得见风险、算得出趋势、做得到最优运维”，推动轨道工程向更安全、更可靠、更智能的方向发展。
主持在研项目2项，以第一/通讯作者在Advanced Engineering Informatics、Applied Energy、Engineering Applications of Artificial Intelligence、铁道学报等国内外重要期刊发表论文14篇（其中SCI区期刊11篇）。
招生信息｜欢迎加入团队
长期招收博士与硕士研究生，欢迎对人工智能+轨道交通交叉方向感兴趣、具有探索精神和科研热情的同学加入。研究内容既面向国家重大交通基础设施需求，也兼具计算机智能、数据科学等前沿技术的应用场景。优秀学生可推荐至海外高校联合培养（含美国、加拿大等高校合作渠道）。
如果你希望：
·在轨道交通智能化、风险智能预测等领域打下扎实科研基础
·接触AI、数据驱动建模、结构动力学、工程风险管理等跨学科内容
·在国际高水平期刊发表科研成果
·在一个支持创新、鼓励探索的团队中快速成长
欢迎邮件联系我，期待与你一起探索轨道交通基础设施智能化的未来！
教育经历
2019.6-2024.10美国罗格斯大学土木与环境工程博士研究生
2016.9-2019.6西南交通大学道路与铁道工程硕士研究生
2012.9-2016.6西南交通大学土木工程本科生
工作经历
2025.1-2025.9 同济大学交通学院助理研究员
2025.9至今同济大学交通学院特聘研究员
获奖情况
2025年获上海市海外高层次人才（青年）
期刊论文
23. Gao, T., Wang, Y.,Wang, X*(2026). Multi-objective optimization of rail welded joint grinding in railroad tracks via reinforcement learning. Engineering Applications of Artificial Intelligence, 164, 113386.
22. Zhou, J., Xue, M.,Wang, X.*, Zhai, G., Tian, C., & Wu, M. (2025). Online estimation and validation of wheel–rail braking adhesion based on negative gradient iteration. Vehicle System Dynamics, 1–25.
21.Wang, X.& Bai, Y. (2025). A multisource data fusion approach for predicting the deterioration of sign structures along highways. Journal of Infrastructure Systems
20.Wang, X., Dai, J., & Liu, X. (2025). A spatial-temporal neural network based on ResNet-Transformer for predicting railroad broken rails. Advanced Engineering Informatics, 65, 103126.
19. Wang J., Xu Y., &Wang X.*(2025) Research progress on interface damage of ballastless track structures in high-speed railways. Construction and Building Materials, 489,142258
18.Wang, X.& Bai, Y. (2025). Multisource data-driven approach for predicting the deterioration of high mast light poles along highways. Journal of Infrastructure Systems, 31(1), 04024036.
17.Wang, X., Liu, X., & Bai, Y. (2024). Prediction of the temperature of diesel engine oil in railroad locomotives using compressed information-based data fusion method with attention-enhanced CNN-LSTM. Applied Energy 367, 123357.
16. Yang C.,Wang, X. *, & Nassif, H. (2024). Impact of environmental conditions on predicting condition rating of concrete bridge decks. Transportation Research Record, 0(0).
15. Kang, D., Dai, J., Liu, X., Bian, Z., Zaman, A. &Wang, X.(2024). Estimating the occurrence of broken rails in commuter railroads with machine learning algorithms. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 09544097241280848.
14.Wang, X., Bai, Y., & Liu, X. (2023). Prediction of foot-by-foot railroad track geometry using a hybrid CNN-LSTM model. Advanced Engineering Informatics 58, 102235.
13.Wang, X., Liu, X., & Euston, T. L. (2023). Relationship between track geometry defect occurrence and substructure condition: A case study on one passenger railroad in the United States. Construction and Building Materials, 365, 130066.
12.Wang, X., Liu, X., & Bian, Z. (2022). A machine learning based methodology for broken rail prediction on freight railroads: A case study in the United States. Construction and Building Materials, 346, 128353.
11. Xu, G., Gutierrez, M., Arora, K., &Wang, X.(2022). Viscoplastic response of deep tunnels based on a fractional damage creep constitutive model. Acta Geotechnica. 17, 613–633
10. Xu, G., He, C. &Wang, X.(2020). Mechanical behavior of transversely isotropic rocks under uniaxial compression governed by micro-structure and micro-parameters. Bulletin of Engineering Geology and the Environment, 79, 1979–2004
9. Wang, Y., Wang, P.,Wang, X., & Liu, X. (2018). Position synchronization for track geometry inspection data via big-data fusion and incremental learning. Transportation Research Part C, 93: 544-565.
8.汪鑫,王平,陈嵘,高原&刘潇潇.(2020).基于多次波形匹配的高速铁路动检数据里程误差评估与修正.铁道学报(02),110-116.
7.王平,汪鑫,王源&张荣鹤.(2020).基于高铁轨道不平顺的车轮不圆顺识别模型.西南交通大学学报(04),681-687+678.
6.王平,高天赐,汪鑫,杨翠平&王源.(2020).基于拟合平纵断面的铁路特大桥梁线路平顺性评估.西南交通大学学报(02),231-237+272.
5.陈嵘,方嘉晟,汪鑫,徐井芒&崔大宾.(2019).车轮型面演变对高速道岔区轮轨接触行为影响分析.铁道学报(05),101-108.
4.汪鑫,高天赐,方嘉晟&王平.(2018).基于时间历程的高速铁路轨道不平顺异常值处理算法.铁道科学与工程学报(12),3029-3036.
3.汪鑫,王源,王平&王沂峰.(2018).高速铁路动检车检测数据里程误差评估与修正.铁道标准设计(07),46-51.
2.徐金辉,汪鑫,黄大维&王彪.(2018).CRTSⅡ型板式轨道参数对车辆频率响应的影响.铁道工程学报(01),62-69+94.
1.张荣鹤,王平,汪鑫&徐井芒.(2018).轨道不平顺作用下动车组安全运行速度限值研究.铁道标准设计(10),62-67+78.
学术会议汇报
4.Wang, X., Bai, Y.. A Data-Driven Approach for Predicting the Deterioration of Highway Ancillary Structures: Case Study on High Mast Light Pole. Transportation Research Board, 103rd Annual Meeting, Washington, DC. January 2024
3.Wang, X., Yang, C.. Impact of Environmental Conditions on Predicting Condition Rating of Concrete Bridge Decks. Transportation Research Board, 103rd Annual Meeting, Washington, DC. January 2024
2.Wang, X., & Zaman, A.. Machine Learning Based Broken Rail Prediction on Freight Railroads: Methodology and A Case Study in the United States. AREMA 2022 Annual Conference & Expo. Denver, August 2022.
1.Wang, X., Zaman, A., & Liu, X.. Artificial Intelligence Aided Broken Rail Prediction. FRA Track & Railroad Workplace Safety Symposium, St. Louis, April 2022.