童峥

（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, Y Zhang, T Ma. Evidential Multi-model Fusion Approach for Cautious Three-Dimensional Object Detection in Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 2025.

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. 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 Multi-model Fusion Approach for Cautious Three-Dimensional Object Detection in Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 2025.

3. 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. 

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

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

6. 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. 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.

2. 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.

3. 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.

4. 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.

5. 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.

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

7. 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. 

8. 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)

9. 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.

10. 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.

11. 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.

12. 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.

13. 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. 

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. 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. DOI

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. DOI

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. DOI

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. DOI

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. DOI

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. DOI

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.