• 2018.09-2022.03，法国索邦大学-贡比涅技术大学计算机专业，博士，导师：Thierry Denoeux、Philippe Xu

• 2015.09-2018.06，长安大学道路与铁道工程，硕士，导师：沙爱民

• 2011.08-2015.06，东北林业大学土木工程专业，学士

2022.06-至今，bw必威西汉姆联官方网站，副教授

人工智能与证据理论：证据理论与证据深度学习、分布式大模型提示学习与专业导向微调方法、不确定性推理与决策方法；

人工智能道路工程应用：公路智能无损检测、公路智能养护决策系统。

1. 国家自然科学基金青年科学基金项目，“基于雷达信号全波形解译的沥青路面结构裂缝空间重构方法研究”，2024.01-2026.12，主持。

2. 江苏省青年托举项目，“基于证据深度学习与全波形解译的道路塌陷反演与预警”，2024.09-2026.09，主持。

3. 江苏省交通运输科技与成果转化项目专题二，“公路工程试验检测智能化数据采集与数字化管理平台研发”，2024.07-2024.10，参与项目，主持专题。

4. 新疆维吾尔自治区重点研发项目，“荒漠区空地协同公路路面智能健康监测系统研发与应用”，2022.01-2025.12，参与项目，主持课题。

5. 江西省南昌县（小蓝经开区）2024年“揭榜挂帅”技术需求项目，“基于三维快速采集与自动化重构技术的路面抗滑性能智能检测技术研究”，2025.05-2027.12，主持。

6. 丝绸之路经济带创新驱动发展试验区、乌昌石国家自主创新示范区科技发展计划，“道路服役性能轻量化检测设备与智能化软件研究”，2025.08-2027.8，主持。

7. 公路交通环境保护技术交通运输行业重点实验室开放基金课题，“基于证据深度神经网络的城市立体道路交通噪声声源监测与辨析方法研究”，2023.03-2024.03，主持。

8. 江苏省交通建设项目，“常熟市多层级公路智能养护决策系统”，2023.11-2024.04，主持。

9. 江苏省交通建设项目，“城市道路塌陷灾变机理与监测预警关键技术研究”，2024.01-2025.12，主持。

10. 安徽省交通建设项目，“亳阜高速全寿命周期智能养护决策系统研究项目”，2023.05-2024.12，主持。

11. 山西省交通建设项目，“高速公路路面和内部损坏一体化检测技术研究”，2023.03-2024.12，主持。

12. 中国高校产学研创新基金，“基于BIM技术的智慧公路工程实训平台开发与应用研究”，2023.06-2024.05。第二完成人。

（1）第一发明人

1. 童峥,佘旭晖,张伟光,马涛,袁文博,王康南.基于三维激光点云与深度神经网络的路面病害自动分割与测量方法及系统.202310606129.6.

2. 童峥;王康南;马涛;张伟光. 一种基于多目事件型相机的路面宏观纹理空间重构方法.202310605989.8.

3. 童峥,方云峰,马涛. 一种基于渗流场与电磁场耦合的城市道路管道漏水电磁响应正演方法.202311390030.3.

4. 童峥,黑天晴,谢志伟,马涛,马成云,常杰,张伟光. 一种基于证据与需求的两阶段项目级养护决策方法.202510357070.0.

5. 童峥,周浩川,茆海阳,马涛. 一种基于虚幻引擎的高速公路数字孪生建模方法与系统.202511199062.4.

6. 童峥,茆海阳,周浩川,曹忠露,侯晋芳,李斌,张伟光.一种基于虚幻引擎V5的高速公路养护施工交通影响仿真方法.202511251187.7.

7. 童峥,鲍宇翔,马涛.一种基于提示学习大模型与文本总结模型循环迭代的道路病害成因分析方法.202511175803.5.

（2）其他发明人

1. 李娟,童峥,袁东东,陈忠杰,张钊,高杰.基于多分支并行卷积神经网络的沥青路面病害检测方法.ZL 2020 1 1425015.4.

2. 王振军,童峥,郭豪彦,高杰,王笑风,李刚,胡永斌,李庆庆.碳纤维增强水泥基材料纤维分散性评价模型与评价方法.ZL 2019 1 0870821.3.

3. 汪祝庆,童峥,范剑伟,谢瀁岭,曹永祥,黄子恒.一种公路工程检测自动化数据采集系统.ZL 2024 1 1794599.0

4. 刘存强,童峥,袁东东,吕锦辉,高自强,高杰.一种基于多视角深度学习的路面宏观纹理重建方法.ZL 2020 1 1423558.2.

5. 王振军,童峥,王笑风,周亮,霍金阳,杨博,彭冲,马玉薇.一种基于深度学习的碳纤维增强水泥基材料的设计方法.ZL 2020 1 0217536.4.

1. 马涛,童峥,张伟光,等.公路路基路面三维探地雷达检测规程,江苏省地方标准,江苏省交通运输厅（归口单位）,2026.

2. 马涛,张伟光,童峥.机场道面表面健康状态智能检测技术.科学出版社,2025.

（1）代表作

1. Z Tong, Y Zhang, T Ma. Guiding GPT models for specific one-for-all tasks in ground penetrating radar. Automation in Construction, 2025, 171, 105979.

2. Z Tong, Y Zhang, T Ma. Evidential transformer for buried object detection in ground penetrating radar signals and interval‐based bounding box. Computer-Aided Civil and Infrastructure Engineering, 2025.

3. Z Tong, Tao Ma, Weiguang Zhang, Ju Huyan. Evidential transformer for pavement distress segmentation. Computer‐Aided Civil and Infrastructure Engineering, 2023.

4. T Hei, Z Lin, Z Dong, Z Tong*, T Ma. Capturing uncertainty intuition in road maintenance decision‐making using an evidential neural network. Computer-Aided Civil and Infrastructure Engineering, 2024.

5. K Wang, T Ma, Y Yang, Z Tong*. Three-dimensional reconstruction of asphalt pavement macrotexture using event camera and evolved recurrent convolution network. Automation in Construction, 2025, 171, 106007.

（2）证据深度学习与大模型

1. Zhengan Wu, Tao Ma, Kangnan Wang, Zheng Tong*. Event-based real-time detection of road auxiliary facility using semantic-spatial fusion and edge-guided attention. Measurement, 2026, 278, 121731.

2. Zhiwei Xie, Z Tong, Tao Ma, Tianqing Hei. Multi-objective decision-making method for road network-level maintenance plans based on Hadamard product and non-dominated sorting genetic Algorithm-III algorithm. Engineering Applications of Artificial Intelligence, 2025, 157, 111292.

3. Z Tong, Y Zhang, T Ma. Guiding GPT models for specific one-for-all tasks in ground penetrating radar. Automation in Construction, 2025, 171, 105979.

4. Z Tong, Philippe Xu, and Thierry Denœux. Fusion of evidential CNN classifiers for image classification. In the 2021 International Conference on Belief Functions, pp. 168-176. Shanghai, China. 

5. Z Tong, Philippe Xu, and Thierry Denœux. Evidential fully convolutional network for semantic segmentation. Applied Intelligence 51, no. 9 (2021): 6376-6399. 

6. Z Tong, Philippe Xu, and Thierry Denœux. An evidential classifier based on Dempster-Shafer theory and deep learning. Neurocomputing 450 (2021): 275-293. 

7. Z Tong, Philippe Xu, and Thierry Denœux. ConvNet and Dempster-Shafer theory for object recognition. Processing in the 6th International Conference on Scalable Uncertainty Management, pp. 368-381. Compiegne, France, 2019.

（3）智能无损检测与养护决策

1. T Hei, Z Tong, Z Xie, T Ma. An all-in-one performance prediction model for pavement management engineering based on Bayesian Neural Network. Advanced Engineering Informatics, 2026, 71, 104413.

2. Y Zhang, J Li, Z Tong, W Zhang, X Shen. A direction-aware and expert-inspired network for internal crack size detection using on-site ground penetrating radar data. Engineering Applications of Artificial Intelligence, 2026 165, 113414.

3. Y Fang, T Hei, Z Tong, T Ma. Study on automated detection methods of shallow surface soil water content based on GPR signal level. Remote Sensing of Environment, 2025, 331, 115003.

4. J Li, T Ma, Z Tong*, Y Zhang, E Zhang, Zhiwei Xie. Electric field intensity distribution and sparse convolutional based networks for three-dimensional structural crack detection. Measurement, 2025, 160, 119758.

5. J Liu, Y Zhang, L Song, Z Tong*. SCB-ADAE: An attention-based deep autoencoder for ground penetrating radar signal denoising. Engineering Applications of Artificial Intelligence, 2025, 160: 111902.

6. Y Fang, Z Tong, T Hei, S Wang, T Ma. Deep learning applications in ground-penetrating radar inversion: A review. Measurement, 2025, 119399.

7. Z Xie, Z Tong, T Ma, T Hei. Multi-objective decision-making method for road network-level maintenance plans based on Hadamard product and non-dominated sorting genetic Algorithm-III algorithm. Engineering Applications of Artificial Intelligence, 2025157, 111292.

8. E Zhang, T Ma, H Yang, J Li, Z Xie, Z Tong*. Milepost-to-Vehicle Monocular Depth Estimation with Boundary Calibration and Geometric Optimization. Electronics, 2025, 14 (17), 3446.

9. X Li, M Su, Y Zhu, S Ma, S Liu, Z Tong*. Evidential Interpretation Approach for Deep Neural Networks in High-Frequency Electromagnetic Wave Processing. Electronics, 2025 14 (16), 3277.

10. Y Zhang, Xiyuan Shen, Jun Lin, Z Tong, Yaoguo Fu, Weiguang Zhang, Tao Bai, and Hanglin Cheng. Confidence level-based size estimation of internal crack using multi-trace ground penetrating radar. Construction and Building Materials 474 (2025): 141124.

11. Z Tong, Y Zhang, T Ma. Guiding GPT models for specific one-for-all tasks in ground penetrating radar. Automation in Construction, 2025, 171, 105979.

12. Z Tong, Y Zhang, T Ma. Evidential transformer for buried object detection in ground penetrating radar signals and interval‐based bounding box. Computer-Aided Civil and Infrastructure Engineering, 2025.

13. K Wang, T Ma, Y Yang, Z Tong*. Three-dimensional reconstruction of asphalt pavement macrotexture using event camera and evolved recurrent convolution network. Automation in Construction, 2025, 171, 106007.

14. Z Tong, Y Zhang, T Ma. Permittivity measurement with uncertainty quantification in cement-based composites using ENNreg-ANet and high-frequency electromagnetic waves. Measurement, 2024, 116537.

15. T Hei, Z Lin, Z Dong, Z Tong*, T Ma. Capturing uncertainty intuition in road maintenance decision‐making using an evidential neural network. Computer-Aided Civil and Infrastructure Engineering, 2024.

16. Y Zhang, Z Tong, X She, S Wang, W Zhang, J Fan, H Cheng, H Yang. SWC-Net and Multi-Phase Heterogeneous FDTD Model for Void Detection Underneath Airport Pavement Slab. IEEE Transactions on Intelligent Transportation Systems, 2024.

17. Z Tong, Tao Ma, Weiguang Zhang, Ju Huyan. Evidential transformer for pavement distress segmentation. Computer‐Aided Civil and Infrastructure Engineering, 2023. 

18. Yiming Zhang, Fan Bao, Z Tong, Tao Ma, Weiguang Zhang, Jianwei Fan, Xiaoming Huang. Radar wave response of slab bottom voids in heterogeneous airport concrete pavement. Journal of Southeast University (Natural Science Edition) 53(1) (2023): 137-148. 

19. Tao Ma, Zheng Tong*, Yiming Zhang, Weiguang Zhang. A three-dimensional reconstruction method of pavement macro-texture using a multi-view deep neural network. China Journal Highway Transportation, 2023, 36(3), 1-11. (in Chinese)

20. Wanli Ye, Wei Jiang, Z Tong*, Dongdong Yuan, and Jingjing Xiao. Convolutional neural network for pothole detection in asphalt pavement. Road materials and pavement design 22, no. 1 (2021): 42-58.

21. Handuo Yang, Ju Huyan, Tao Ma, Z Tong, Chengjia Han, and Tianyan Xie. Novel Computer Tomography image enhancement deep neural networks for asphalt mixtures. Construction and Building Materials 352 (2022): 129067.

22. Z Tong, Jie Gao, and Dongdong Yuan. Advances of deep learning applications in ground-penetrating radar: A survey. Construction and Building Materials 258 (2020): 120371.

23. Jie Gao, Dongdong Yuan, Z Tong*, Jiangang Yang, and Di Yu. Autonomous pavement distress detection using ground penetrating radar and region-based deep learning. Measurement 164 (2020): 108077.

24. Z Tong, Dongdong Yuan, Jie Gao, Yongfeng Wei, and Hui Dou. Pavement-distress detection using ground-penetrating radar and network in networks. Construction and Building Materials 233 (2020): 117352. 

25. Z Tong, Dongdong Yuan, Jie Gao, and Zhenjun Wang. Pavement defect detection with fully convolutional network and an uncertainty framework. Computer‐Aided Civil and Infrastructure Engineering 35, no. 8 (2020): 832-849.

26. Z Tong, Jie Gao, and Haitao Zhang. Innovative method for recognizing subgrade defects based on a convolutional neural network. Construction and Building Materials 169 (2018): 69-82.

27. Z Tong, Jie Gao, Aimin Sha, Liqun Hu, and Shuai Li. Convolutional neural network for asphalt pavement surface texture analysis. Computer‐Aided Civil and Infrastructure Engineering 33, no. 12 (2018): 1056-1072.

28. Z Tong, Jie Gao, Zhenqiang Han, and Zhenjun Wang. Recognition of asphalt pavement crack length using deep convolutional neural networks. Road Materials and Pavement Design 19, no. 6 (2018): 1334-1349.

29. Aimin Sha, Z Tong, and Jie Gao. Recognition and measurement of pavement disasters based on convolutional neural networks. China Journal of Highway and Transport 31, no. 1 (2018): 1.

30. Z Tong, Jie Gao, and Haitao Zhang. Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks. Construction and Building Materials 146 (2017): 775-787.

（3）深度学习与材料工程

1. Z Tong, Zhenjun Wang, Xiaofeng Wang, Yuwei Ma, Haoyan Guo, and Cunqiang Liu. Characterization of hydration and dry shrinkage behavior of cement emulsified asphalt composites using deep learning. Construction and Building Materials 274 (2021): 121898. 

2. Z Tong, Jinyang Huo, and Zhenjun Wang. High-throughput design of fiber reinforced cement-based composites using deep learning. Cement and Concrete Composites 113 (2020): 103716. 

3. Z Tong, Jie Gao, Zhenjun Wang, Yongfeng Wei, and Hui Dou. A new method for CF morphology distribution evaluation and CFRC property prediction using cascade deep learning. Construction and Building Materials 222 (2019): 829-838.

4. Dongdong Yuan, Wei Jiang, Z Tong*, Jie Gao, Jingjing Xiao, and Wanli Ye. Prediction of electrical conductivity of fiber-reinforced cement-based composites by deep neural networks. Materials 12, no. 23 (2019): 3868. 

5. Z Tong, Haoyan Guo, Jie Gao, and Zhenjun Wang. A novel method for multi-scale carbon fiber distribution characterization in cement-based composites. Construction and Building Materials 218 (2019): 40-52. 

6. Hai Liu, Aimin Sha, Z Tong*, and Jie Gao. Autonomous microscopic bunch inspection using region-based deep learning for evaluating graphite powder dispersion. Construction and Building Materials 173 (2018): 525-539. 

7. Z Tong, Jie Gao, and Haitao Zhang. Innovation for evaluating aggregate angularity based upon 3D convolutional neural network. Construction and Building Materials 155 (2017): 919-929. 

（4）其他

1. Jianying Hu, Fan Bao, Ju Huyan, Yu Zhu, Z Tong, and Weiguang Zhang. Risk Evaluation of Airport Safety during Non-stop Construction Using Fuzzy Analytical Hierarchy Process and Bayesian Belief Network. Advance Researches in Civil Engineering 4, no. 2 (2022): 10-23.

2. Weiguang Zhang, Kamal Nasir Ahmad, Z Tong, Zhaoguang Hu, Haoyang Wang, Meng Wu, Kai Zhao, Shunxin Yang, Hassan Farooq, and Louay N. Mohammad. In-Time Density Monitoring of In-Place Asphalt Layer Construction via Intelligent Compaction Technology. Journal of Materials in Civil Engineering 35, no. 1 (2023): 04022386.

3. Chengjia Han, Fanlong Tang, Tao Ma, Linhao Gu, and Z Tong. Construction quality evaluation of asphalt pavement based on BIM and GIS. Automation in Construction 141 (2022): 104398.

4. Dongdong Yuan, Wei Jiang, Jingjing Xiao, Z Tong, Meng Jia, Jinhuan Shan, and Aboudou Wassiou Ogbon. Assessment of the Aging Process of Finished Product–Modified Asphalt Binder and Its Aging Mechanism. Journal of Materials in Civil Engineering 34, no. 8 (2022): 04022174. DOI

5. Wei Jiang, Dongdong Yuan, Z Tong, Aimin Sha, Jingjing Xiao, Meng Jia, Wanli Ye, and Wentong Wang. Aging effects on rheological properties of high viscosity modified asphalt. Journal of Traffic Transportationg (English Edition) 20210322, no. 002 (2021).

6. Jie Gao, Aimin Sha, Yue Huang, Zhuangzhuang Liu, Liqun Hu, Wei Jiang, Di Yun, Z Tong, and Zhenjun Wang. Cycling comfort on asphalt pavement: Influence of the pavement-tyre interface on vibration. Journal of cleaner production 223 (2019): 323-341.

7. Jie Gao, Aimin Sha, Yue Huang, Liqun Hu, Z Tong, and Wei Jiang. Evaluating the cycling comfort on urban roads based on cyclists’ perception of vibration. Journal of Cleaner Production 192 (2018): 531-541.

8. Jie Gao, Aimin Sha, Zhenjun Wang, Z Tong, and Zhuangzhuang Liu. Utilization of steel slag as aggregate in asphalt mixtures for microwave deicing. Journal of Cleaner Production 152 (2017): 429-442.

9. Zhenqiang Han, Aimin Sha, Z Tong, Zhuangzhuang Liu, Jie Gao, Xiaolong Zou, and Dongdong Yuan. Study on the optimum rice husk ash content added in asphalt binder and its modification with bio-oil. Construction and Building Materials 147 (2017): 776-789. DOI

10. Zhuangzhuang Liu, Aimin Sha, Liqun Hu, Yongwei Lu, Wenxiu Jiao, Zheng Tong, and Jie Gao. Kinetic and thermodynamic modeling of Portland cement hydration at low temperatures. Chemical Papers 71, no. 4 (2017): 741-751.

1. 2025年云南省科学技术进步二等奖，“高原山区公路数智一体化管养关键技术及应用”，排2

2. 2025年中国公路学会科学技术一等奖，“道路基础设施病害精准智能检测识别关键技术、装备与工程应用”，排3

3. 2025年陕西高等公司科学技术研究优秀成果特等奖，“道路基础设施数智化高精度检测关键技术及工程应用”，排2

4. 2025年云南交通科学技术特等奖，“高原山区公路数智一体化管养关键技术及应用”，排2

5. 2024年西汉姆联官方网站青年五四奖章集体奖，排2

6. 2023年中国公路学会科学技术一等奖，“高速公路沥青路面精细化智能无损检测技术及应用”，排3

7. 2023年度中国交通运输协会科学技术一等奖，“高速公路沥青路面智慧检测与养护决策关键技术与应用”，排3

8. 7th International Conference on Belief Functions (2021) Best paper.

（1）教改项目

1. 2025年江苏省高校人工智能通识公司产品改革研究专项课题，“交通运输专业产教融合导向的AI通识实践教学体系研究”，主持

2. 2025年西汉姆联官方网站“人工智能+教学”试点本科课程建设项目，“人工智能基础”，主持

3. 中国高校产学研创新基金-北创助教项目，“基于BIM技术的智慧公路工程实训平台开发与应用研究”，2/12参与

（2）本科生课程

1. 大一通识课程《人工智能通识导论》

2. 大二专业基础课程《人工智能基础》（2025年“人工智能+教学”试点本科课程）

（3）研究生课程

1. 《道路工程深度学习技术》（南京）

2. 《交通基础设施人工智能方法》（苏州）、《智慧机场检测原理》（苏州）

（1）博士招生计划1名

    方向：分布式大模型的证据一致性算法及无人驾驶应用

    基本要求：python或C++编程基础、已在“AI+交通运输工程”或“AI+交通基础设施”领域发表高水平论文不少于1篇。

（2）硕士招生计划2-3名

    方向1：基于分布式大模型的多车路径规划方法（苏州校区）

    基本要求：python或C++编程基础

    方向2：大模型驱动的公路轻量化检测系统

    基本要求：python或C++编程基础

    方向3：基于大模型与虚幻引擎的道路养护全流程设计方法

    基本要求：python或C++编程基础