Faculty
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》 Contact Information

Name:Yue Pan

Phone:021-69585580

Email:pan_yue@tongji.edu.cn

College:College of Electronic and Information Engineering, Tongji University

Title:Dr.

》  Research Fields

§ Structural Health Monitoring

§ Computer Vision

§ Machine Learning

§ Structural Inspection Robotics

》  Courses

§ Machine learning and Big data processing

§ Data Mining

》  Projects

§ The National Key Research and Development Program of China (Grant No. 2021YFF0501004)

§ Zhejiang Provincial Department of Transportation Science and Technology Plan Project (Grant No.2020048)

§ Shanghai Peak discipline construction Plan (Grant No. 2021-CE-09)

》  Publications

§ Y. Pan, D. Wang*, Y. Dong and B. Peng, "A Novel Vision-Based Framework for Identifying Dynamic Vehicle Loads on Long-Span Bridges: A Case Study of Jiangyin Bridge, China," IEEE Transactions on Intelligent Transportation Systems, 2021, DOI: 10.1109/TITS.2021.3094504.

§ Y. Pan, Y. Ma, Y. Dong, Z. Gu and D. Wang*, "A Vision-Based Monitoring Method for The Looseness of High-Strength Bolt," IEEE Transactions on Instrumentation and Measurement, Vol. 70, pp. 1-14, 2021, Art no. 5013914, DOI: 10.1109/TIM.2021.3101316.

§ Pan, Y., Y. Dong, D. Wang*, A. Chen And Z. Ye., "Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges using UAV-Based Photogrammetric Point Clouds" Remote Sensing, 2019; Vol. 11, No. 10: 1204. DOI: 10.3390/RS11101204.

§ Pan, Y., Wang, D., Shen, X., Xu, Y. and Pan, Z, "A Novel Computer Vision-Based Monitoring Methodology for Vehicle-Induced Aerodynamic Load On Noise Barrier" Structural Control & Health Monitoring. 2018; 25: e2271. DOI: 10.1002/STC.2271.

§ Yiqing Dong, Yue Pan*, Tianzheng Cheng, Dalei Wang. "Corrosion detection and evaluation for steel wires based on a multi-vision scanning system" Construction and Building Materials,2021;DOI: 10.1016/j.conbuildmat.2021.125877.

§ Cheng Xiang, Dalei Wang, Yue Pan*, Airong Chen, Xiaoyi Zhou, Yiquan Zhang. "Accelerated topology optimization design of 3D structures based on deep learning" Structural and Multidisciplinary Optimization, 2021.

§ D. Wang, C. Xiang, Y. Pan*, A. Chen, X. Zhou and Y. Zhang, "A Deep Convolutional Neural Network for Topology Optimization with Perceptible Generalization Ability" Engineering Optimization. 2021; DOI: 10.1080/0305215x.2021.1902998.

§ D. Wang, Y. Dong, Y. Pan* and R. Ma, "Machine Vision-Based Monitoring Methodology for the Fatigue Cracks in U-Rib-to-Deck Weld Seams," in IEEE Access, vol. 8, pp. 94204-94219, 2020, DOI: 10.1109/ACCESS.2020.2995276.

§ D. Wang, Y. Zhang, Y. Pan*, B. Peng, H. Liu and R. Ma, "An Automated Inspection Method for the Steel Box Girder Bottom of Long-Span Bridges Based on Deep Learning," in IEEE Access, vol. 8, pp. 94010-94023, 2020, DOI: 10.1109/ACCESS.2020.2994275.

§ Chen, A., Fang, X., Pan, Z.*, Wang, D., Pan, Y, Peng, B.. Engineering practices on surface damage inspection and performance evaluation of concrete bridges in China. Structural Concrete. 2021; 1– 16. DOI:10.1002/SUCO.202100158.

§ Ma, Rujin, Zhen Zhang, Yiqing Dong*, and Yue Pan. "Deep Learning Based Vehicle Detection and Classification Methodology Using Strain Sensors under Bridge Deck" Sensors,2020, no. 18: 5051. DOI: 10.3390/s20185051.

§ 董一庆,王达磊,潘玥*,李晓娅.结构表观病害人工标记数据标准化批处理方法[J]. 华南理工大学学报(自然科学版). 2021. doi:10.12141/j.issn.1000-565X.210016.

§ 马如进,董一庆,潘子超*,潘玥.基于消费级无人机的古桥三维重构分析[J].华南理工大学学报(自然科学版),2019,47(06):94-100+126.

§ 王达磊,彭博,潘玥,陈艾荣*. 基于深度神经网络的锈蚀图像分割与定量分析[J]. 华南理工大学学报(自然科学版).2018,46(12):121-127.

§ 陈艾荣,潘玥,王达磊,马如进. 大数据时代的桥梁维护与安全[J]. 上海公路. 2014(01):17-23.

§ X. L. Zhuang, D. L. Wang, B. Peng, Y. Pan*, and H. B. Sun, “Inspection and evaluation of apparent damage in concrete pier based on computer vision,” in Life-Cycle Civil Engineering: Innovation, Theory and Practice, CRC Press, 2021, pp. 1566–1572.

§ Z. X. Gu, Y. Q. Dong, D. L. Wang, Y. Pan*, and X. D. Xu, “A novel system for the looseness monitoring of high-strength bolts based on machine vision,” in Life-Cycle Civil Engineering: Innovation, Theory and Practice, CRC Press, 2021, pp. 1561–1565.

§ Y. L. Ma, Y. Q. Dong, D. L. Wang, Y. Pan*, and S. Q. Zhang, “Full-bridge vehicle identification and tracking method for long-span bridges based on deep learning,” in Life-Cycle Civil Engineering: Innovation, Theory and Practice, CRC Press, 2021, pp. 1605–1610.

§ Y. P. Ji, Y. Q. Dong, D. L. Wang*, R. J. Ma, and Y. Pan, “A vehicle axle group classification approach based on convolutional neural networks,” in Life-Cycle Civil Engineering: Innovation, Theory and Practice, CRC Press, 2021, pp. 1630–1635.

§ Y. Q. Dong, Y. Pan, T. Ma, R. J. Ma, and D.L.Wang*, “A novel system for the monitoring of fatigue cracks in orthotropic steel-box girder,” in Life-Cycle Civil Engineering: Innovation, Theory and Practice, CRC Press, 2021, pp. 1349–1357.

§ D. Wang, B. Peng, and Y. Pan*, “Corrosion segmentation and quantitative analysis based on deep neural networks,” in Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges, CRC Press, 2018, pp. 2881–2888.

§ D. Wang, Y. Pan*, and B. Peng, “Steel box-girder bridge diseases identification based on computer vision system,” in Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges, CRC Press, 2018, pp. 504–511.

§ Y. Pan*, D. L. Wang, A. R. Chen, and R. J. Ma, “A creation method of lane load characteristic on highway bridges based on deck monitoring video integrated with WIM data,” in Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks, CRC Press, 2016, pp. 163–163.

§ D. L. Wang, Y. Pan*, X. Shen, R. J. Ma, and A. R. Chen, “An innovation monitoring method of vehicle induced aerodynamic load on sound barrier integrated with photogrammetry and image analysis,” in Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks, CRC Press, 2016, pp. 232–232.

§ A. Chen, Y. Pan*, Z. Pan, and R. Ma, “Vehicle load identification of long-span bridges based on dynamic image recognition: Theoretical research and application in Sutong Bridge,” in Life-Cycle of Civil Engineering Systems, CRC Press, 2014, pp. 1017–1020.


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