铁路有砟道床脏污识别与劣化特征分析，中国铁路上海局集团有限公司科学技术研究所，2025年9月-2026年12月，主持

考虑轮对蠕滑条件下的动力学仿真程序优化及验证，中国铁道科学研究院集团有限公司，2025年12月-2026年12月，主持

铁路有砟道床脏污识别与劣化特征分析，中国铁路上海局集团有限公司科学技术研究所，2025年9月-2026年12月，项目负责人

考虑轮对蠕滑条件下的动力学仿真程序优化及验证，中国铁道科学研究院集团有限公司，2025年12月-2026年12月，项目负责人

期刊论文

23.Gao, T., Wang, Y., Wang, X* (2026). Multi-objective optimization of rail weldedjoint grinding in railroad tracks via reinforcement learning. EngineeringApplications of Artificial Intelligence, 164, 113386.

22. Gao, Y. & Wang, X.* (2026). Prediction of safety limits for rail weld irregularities in high-speed turnouts using a CNN-LSTM hybrid model. Advanced Engineering Informatics,1–17.

21. Wang, X. & Bai, Y. (2025). 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 forpredicting 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 locomotivesusing compressed information-based data fusion method with attention-enhancedCNN-LSTM. Applied Energy 367, 123357.

16. Yang C., Wang, X. *, & Nassif, H.(2024). Impact of environmental conditions on predicting condition rating ofconcrete 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 incommuter railroads with machine learning algorithms. Proceedings of theInstitution 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 hybridCNN-LSTM model. Advanced Engineering Informatics 58, 102235.

13. Wang, X., Liu, X., & Euston, T. L.(2023). Relationship between track geometry defect occurrence and substructurecondition: 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 onfreight railroads: A case study in the United States. Construction and BuildingMaterials, 346, 128353.

11. Xu, G., Gutierrez, M., Arora, K., &Wang, X. (2022). Viscoplastic response of deep tunnels based on a fractionaldamage creep constitutive model. Acta Geotechnica. 17, 613–633 

10. Xu, G., He, C. & Wang, X. (2020).Mechanical behavior of transversely isotropic rocks under uniaxial compressiongoverned by micro-structure and micro-parameters. Bulletin of EngineeringGeology and the Environment, 79, 1979–2004

9. Wang, Y., Wang, P., Wang, X., & Liu,X. (2018). Position synchronization for track geometry inspection data viabig-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

Wang,X., Bai, Y.. A Data-Driven Approach forPredicting the Deterioration of Highway Ancillary Structures: Case Study on High Mast Light Pole. Transportation Research Board, 103rd Annual Meeting,Washington, DC. January 2024

Wang,X., Yang, C.. Impact of EnvironmentalConditions on Predicting Condition Rating of Concrete Bridge Decks.Transportation Research Board, 103rd Annual Meeting, Washington, DC. January2024

Wang,X., & Zaman, A.. Machine LearningBased Broken Rail Prediction on Freight Railroads: Methodology and A Case Studyin the United States. AREMA 2022 Annual Conference & Expo. Denver, August2022.

Wang,X., Zaman, A., & Liu, X.. ArtificialIntelligence Aided Broken Rail Prediction. FRA Track & Railroad WorkplaceSafety Symposium, St. Louis, April 2022.