教师名录

【工作经历】

2025.01-至今：美加墨世界杯官方网站，土木工程系，长聘教轨副教授，博士生导师

2021.10-2024.12：美加墨世界杯官方网站，土木工程系，助理教授，博士生导师

【教育背景】

2018.07 – 2021.09：新加坡南洋理工大学，土木工程，博士 （毕业论文：Mining building information modeling (BIM) event logs for improved project management）

2016.08 – 2017.12：美国卡内基梅隆大学，土木工程，硕士（Advanced Infrastructure Systems项目）

2012.09 – 2016.06：同济大学，工程力学，学士

1. 地下工程智能建造与运维/Smart construction engineering and management in underground engineering 

2. 工程信息化/Engineering informatics

3. 数据挖掘与数字孪生/Data mining and digital twin

欢迎对基于人工智能的基础设施系统数字化建造与管理感兴趣的同学积极联系咨询，一起探索新一代信息技术在工程全生命周期管理中的理论发展和实践应用。

（1）《Reliability Engineering & System Safety》SCI期刊客座编辑 
（2）《Smart Construction》、《Intelligent Geoengineering》期刊青年编委 
（3）中国土木工程学会工程风险与保险研究分会青年论坛委员 
（4）中国图学学会BIM专业委员会委员
（5）中国土木工程学会土力学及岩土工程分会青年工作委员会委员
（6）中国岩土力学与工程学会人工智能技术实用化专委会委员

（7）多份SCI杂志审稿人，部分如下：Nature Communications， Automation in Construction， Tunnelling and Underground Space Technology， Advanced Engineering Informatics， Applied Energy， Information Fusion等

【国家及省部级项目】

1. 国家自然科学基金委员会青年科学基金项目：基于数物融合深度学习的深大基坑施工灾变风险在线预测与防控研究，2023-01至2025-12，主持

2. 国家重点研发计划，熔焊工艺与质量实时调控垂域模型技术，2025-11 至 2028-10，子课题负责人

3. 国家重点研发计划：基于多指标融合的建筑与基础设施韧性评价方法，2023-11至2026-10，子课题负责人

4. 上海市“晨光计划”：基于时空图神经网络的深大基坑施工风险态势预控研究，2025-01至2026-12，主持

5. 上海市启明星项目（扬帆专项）：基于多源时序大数据融合的盾构智能掘进动态管控研究，2022-04至2025-03，主持

【横向项目】

6. 中石油国家煤岩气重点实验室开放课题：基于煤岩CT实验数据的割理多尺度表征与智能预测方法，2025-11至2026-10，主持

7. 上海勘察设计研究院（集团）有限公司科研项目：城市地铁隧道表观病害快速识别方法与实现，2023-05至2023-12，主持

8. 上海隧道工程有限公司科研项目：基于多目标优化的束合管幕围护结构设计优化方法，2024-04至2027-06，主持

【地方实验室开放基金】

9. 美加墨世界杯官方网站“AI for Engineering”赋能计划项目（全校10项）：认知大模型与人机协同驱动的城市路网基础设施群韧性演化与提升，2026-1至2027-12，主持

10. 教育部哲学社会科学重点实验室天津大学复杂管理系统实验室开放基金：基于深度强化学习的深大基坑施工人-机协同巡检研究，2024-01至2025-12，主持

11. 建筑能效控制与评估教育部工程研究中心开放基金：基于BIM技术深度强化学习算法的绿色建筑能效优化研究，2023-06至2025-05，主持

12. 智能建筑与建筑节能安徽省重点实验室开放基金：BIM及人工智能驱动下绿色建筑能耗预测与优化研究，2022-05至2024-04，主持

13. 上海市公共建筑和基础设施数字化运维重点实验室开放基金：基于深度学习计算机视觉的施工人员个人防护装备自主识别，2021-12至2022-12，主持

14. 美加墨世界杯官方网站“双一流”建设项目人才科研启动基金，2021-10至2024-09，主持

截止2026年3月，在人工智能赋能地下工程建造与运维管理领域内权威期刊发表高水平SCI论文76篇，其中一作/通讯57篇（中科院TOP期刊55篇），ESI高被引9篇、热点2篇，TOP SCI封面论文2篇，总引用7400余次，H指数36。代表性成果发表于 Automation in Construction（IF: 11.5，17 篇），Tunnelling and Underground Space Technology （IF: 7.4，7 篇），Computer‐Aided Civil and Infrastructure Engineering（IF: 9.1，5 篇，其中 1篇入选封面论文），Advanced Engineering Informatics（IF: 9.9，4 篇），Information fusion（IF: 15.5，2 篇），Underground Space（IF: 8.3，2 篇，其中 1 篇入选封面论文）， Acta Geotechnica（IF：5.7，2 篇）等。一作/通讯SCI论文如下：

【一作或通讯SCI论文】

[57] J. Wei, Y. Pan*, L Zhen, J.-J. Chen, “Data-mechanism hybrid-driven digital twin for spatiotemporal prediction of multiple evolving risk in deep excavation”, Tunnelling and Underground Space Technology, vol. 173, p. 107588, 2026.

[56] C. Zhang, Y. Pan*, Y. Hou, X. Xia, J.-J. Chen, “Physics–Virtual Collaboration Prediction Approach for Interpretable Deformation Prediction in Pre-Support Tunnel Construction”, Underground Space, 2026 (accept)

[55] W. He, Y. Pan*, S. Zhang, G. Ye, J.-J. Chen, “Integrating Machine Learning and Interval Fuzzy AHP for Assessing Metro Station Resilience to Urban Flooding”, Sustainable Cities and Society, vol. 139, p. 107213, 2026.

[54] J. Wei, Y. Pan*, J.-J. Chen, “Physics-informed edge-enhanced temporal graph convolutional network for multi-risk evolution prediction in deep excavation”, Advanced Engineering Informatics, vol. 71, p. 104391, 2026.

[53] W. Lai, Y. Pan*, L. Zhang, J.-J. Chen, J. Qin, “Towards low-carbon construction of metro station foundation pit: A probabilistic digital twin framework with self-supervised learning capability”, Tunnelling and Underground Space Technology, vol. 171, p. 107450, 2026.

[52] X. Zhou, Y. Pan*, J. Qin, J.-J. Chen, “Large language model-enhanced graph neural network for quantile prediction of railway track settlement near deep excavations”, Advanced Engineering Informatics, vol. 69, p. 104105, 2026.

[51] Z. He, Y. Pan*, J.-J. Chen, “Towards automated and uncertainty-aware risk assessment for deep excavation via data-driven pythagorean fuzzy Bayesian network,” Expert Systems with Applications, vol. 300, p. 130391, 2026.

[50] C Zhang, Y Hou, X Xia, J.-J. Chen, Y. Pan*, “Multimodal feature fusion deep learning for spatiotemporal prediction of deformation and environmental impacts in pipe-roof tunnel construction,” Advanced Engineering Informatics, vol. 69, p. 104022, 2026.

[49] Z. He, Y. Pan*, J.-J. Chen, “Data-physics-fused deep learning for risk prediction in soft soil excavations: a case study of Shanghai metro”, Acta Geotechnica, 2025.

[48] J. Wei, Y. Pan*, and J.-J. Chen, "Multivariate fusion-based surrogate modeling for predicting excavation-induced full-field vertical soil displacement," Automation in Construction, vol. 180, p. 106511, 2025.

[47] X. Wang, Y. Pan*, and J.-J. Chen, "Digital twin with uncertainty‐informed deep learning for prompt quantitative risk assessment of deep excavation," Computer‐Aided Civil and Infrastructure Engineering, vol. 40, no. 25, pp. 4226-4252, 2025.

[46] Y. Pan, X. Li, J. Qin, J.-J. Chen, and P. Gardoni, "Towards trustworthy excavation-induced risk warning for adjacent building: A Bayesian reasoning based probabilistic deep learning method," Underground Space, vol. 25, pp. 156-175, 2025.

[45] W. He, Y. Pan*, and J.-J. Chen, "An Enhanced metro resilience assessment combining dynamic cascade-based simulation and deep learning-based attributed graph clustering," IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 10, pp. 14910 – 14926, 2025.

[44] W. He, Y. Pan*, Y Hou, and J.-J. Chen, " Multi-objective optimization framework for generative design of horseshoe-shaped pipe arrangement in pre-stressed underground bundles," Tunnelling and Underground Space Technology, vol. 158, p. 106437, 2025.

[43] H. Yang, L. Wang, Y. Pan*, and J.-J. Chen, "A Teacher-Student Framework Leveraging Large Vision Model for Data Pre-Annotation and YOLO for Tunnel Lining Multiple Defects Instance Segmentation," Journal of Industrial Information Integration, vol. 44, p. 100790, 2025.

[42] C. Zhang, Y. Pan*, and J.-J. Chen, "Image-based prediction for enclosure structure deformation in pipe-roof tunnel construction using a physical-guided and generative deep learning method," Automation in Construction, vol. 171, p. 106002, 2025.

[41] Y. Pan, W. He, and J.-J. Chen, "Spatiotemporal deep learning for multi-attribute prediction of excavation-induced risk," Automation in Construction, vol. 171, p. 105964, 2025.

[40] A. Su, J. Cheng, Y. Wang, and Y. Pan*, "Machine learning-based processes with active learning strategies for the automatic rapid assessment of seismic resistance of steel frames," Structures, vol. 72, p. 108227, 2025.

[39] Y. Pan, X. Zhou, J.-J. Chen, and Y. Hong, "Temporal-spatial-fusion-based risk assessment on the adjacent building during deep excavation," Information Fusion, vol. 114, p. 102653, 2025.

[38] X. Wang, Y. Pan*, and J. Chen, "Digital twin with data-mechanism-fused model for smart excavation management," Automation in Construction, vol. 168, p. 105749, 2024.

[37] L. Zhang, Y. Li, Y. Pan*, and L. Ding, "Advanced informatic technologies for intelligent construction: A review," Engineering Applications of Artificial Intelligence, vol. 137, p. 109104, 2024.

[36] Y. Pan, Y. Shen, J. Qin, and L. Zhang, "Deep reinforcement learning for multi-objective optimization in BIM-based green building design," Automation in Construction, vol. 166, p. 105598, 2024.

[35] Y. Pan, L. Li, J. Qin, J. J. Chen, and P. Gardoni, " Unmanned aerial vehicle–human collaboration route planning for intelligent infrastructure inspection," Computer-Aided Civil and Infrastructure Engineering, vol. 39, no. 14, 2024.

[34] Y. Pan, Z. Wang, L. Sun, and J.-J. Chen, "Dynamic prediction and multi-objective optimization on driving position of tunnel boring machine (TBM): an automated deep learning approach," Acta Geotechnica, pp. 1-26, 2024.

[33] X. Zhou, Y. Pan*, J. Qin, J.-J. Chen, and P. Gardoni, "Spatio-temporal prediction of deep excavation-induced ground settlement: A hybrid graphical network approach considering causality," Tunnelling and Underground Space Technology, vol. 146, p. 105605, 2024.

[32] X. Wang, Y. Pan*, M. Li, and J. Chen, "A novel data-driven optimization framework for unsupervised and multivariate early-warning threshold modification in risk assessment of deep excavations," Expert Systems with Applications, vol. 238, p. 121872, 2024.

[31] Y. Pan, J. Qin, Y. Hou, and J.-J. Chen, "Two-stage support vector machine-enabled deep excavation settlement prediction considering class imbalance and multi-source uncertainties," Reliability Engineering & System Safety, vol. 241, p. 109578, 2024.

[30] X. Li, Y. Pan*, L. Zhang, and J. Chen, "Dynamic and explainable deep learning-based risk prediction on adjacent building induced by deep excavation," Tunnelling and Underground Space Technology, vol. 140, p. 105243, 2023.

[29] Y. Pan, M. Wu, L. Zhang, and J. Chen, "Time series clustering-enabled geological condition perception in tunnel boring machine excavation," Automation in Construction, vol. 153, p. 104954, 2023.

[28] Y. Pan, J. Qin, L. Zhang, W. Pan, and J. J. Chen, "A probabilistic deep reinforcement learning approach for optimal monitoring of a building adjacent to deep excavation, " Computer-Aided Civil and Infrastructure Engineering, vol. 39, no. 5, pp. 656-678, 2024.

[27] Y. Pan, X. Zhou, S. Qiu, and L. Zhang, "Time series clustering for TBM performance investigation using spatio-temporal complex networks," Expert Systems with Applications, vol. 225, p. 120100, 2023. 

[26] Y. Shen and Y. Pan*, " BIM-supported automatic energy performance analysis for green building design using explainable machine learning and multi-objective optimization," Applied Energy, vol. 333, p. 120575, 2023. 

[25] Y. Pan and L. Zhang, "Integrating BIM and AI for Smart Construction Management: Current Status and Future Directions," Archives of Computational Methods in Engineering, pp. 1-30, 2022.

[24] Y. Pan, J. Qin, "A novel probabilistic modeling framework for wind speed with highlight of extremes under data discrepancy and uncertainty," Applied Energy, vol. 326, p. 119938, 2022. 

[23] Y. Pan, L. Zhang, "Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design," Advanced Engineering Informatics, vol. 54, p. 101758, 2022. 

[22] Y. Pan, X. Fu, and L. Zhang, "Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach," Automation in Construction, vol. 141, p. 104386, 2022.

[21] Y. Pan and L. Zhang, "Mitigating tunnel-induced damages using deep neural networks," Automation in Construction, vol. 138, p. 104219, 2022.

[20] Y. Pan and L. Zhang, " Dual attention deep learning network for automatic steel surface defect segmentation," Computer-Aided Civil and Infrastructure Engineering, vol. 37, no. 11, pp. 1468-1487, 2022.

[19] L. Zhang and Y. Pan*, "Information fusion for automated post-disaster building damage evaluation using deep neural network," Sustainable Cities and Society, vol. 77, p. 103574, 2022.

[18] Y. Pan, L. Zhang, J. Unwin, and M. J. Skibniewski, "Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States," Sustainable Cities and Society, vol. 77, p. 103508, 2022.

[17] Y. Pan, L. Zhang, Z. Yan, M. O. Lwin, and M. J. Skibniewski, "Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia," Sustainable cities and society, vol. 75, p. 103254, 2021.

[16] A. W. Z. Chew, Y. Pan, Y. Wang, and L. Zhang, "Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission," Knowledge-Based Systems, vol. 233, p. 107417, 2021.

[15] Y. Pan and L. Zhang, "Automated process discovery from event logs in BIM construction projects," Automation in Construction, vol. 127, p. 103713, 2021.

[14] Y. Pan and L. Zhang, "A BIM-data mining integrated digital twin framework for advanced project management," Automation in Construction, vol. 124, p. 103564, 2021.

[13] Y. Pan and L. Zhang, "Roles of artificial intelligence in construction engineering and management: A critical review and future trends," Automation in Construction, vol. 122, p. 103517,2021.

[12] Y. Pan, L. Zhang, and Z. Li, "Mining event logs for knowledge discovery based on adaptive efficient fuzzy Kohonen clustering network," Knowledge-Based Systems, vol. 209, p. 106482, 2020.

[11] Y. Pan, L. Zhang, J. Koh, and Y. Deng, "An adaptive decision making method with copula Bayesian network for location selection," Information Sciences, vol. 544, pp. 56-77, 2020.

[10] Y. Pan, G. Zhang, and L. Zhang, "A spatial-channel hierarchical deep learning network for pixel-level automated crack detection," Automation in Construction, vol. 119, p. 103357, 2020.

[9] Y. Pan and L. Zhang, "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, vol. 268, p. 114965, 2020.

[8] Y. Pan, L. Zhang, and M. J. Skibniewski, "Clustering of designers based on building information modeling event logs," Computer-Aided Civil and Infrastructure Engineering, vol. 35, no. 7, pp. 701-718, 2020.

[7] Y. Pan, L. Zhang, X. Wu, and M. J. Skibniewski, "Multi-classifier information fusion in risk analysis," Information Fusion, vol. 60, pp. 121-136, 2020.

[6] Y. Pan and L. Zhang, "BIM log mining: Learning and predicting design commands," Automation in Construction, vol. 112, p. 103107, 2020.

[5] Y. Pan and L. Zhang, "BIM log mining: Exploring design productivity characteristics," Automation in Construction, vol. 109, p. 102997, 2020.

[4] Y. Pan, L. Zhang, X. Wu, K. Zhang, and M. J. Skibniewski, "Structural health monitoring and assessment using wavelet packet energy spectrum," Safety Science, vol. 120, pp. 652-665, 2019.

[3] Y. Pan, S. Ou, L. Zhang, W. Zhang, X. Wu, and H. Li, "Modeling risks in dependent systems: A Copula-Bayesian approach," Reliability Engineering and System Safety, vol. 188, pp. 416-431, 2019.

[2] Y. Pan, L. Zhang, Z. Li, and L. Ding, "Improved fuzzy Bayesian network-based risk analysis with interval-valued fuzzy sets and DS evidence theory," IEEE Transactions on Fuzzy Systems, vol. 28, no. 9, pp. 2063-2077, 2019.

[1] Y. Pan, L. Zhang, X. Wu, W. Qin, and M. J. Skibniewski, "Modeling face reliability in tunneling: A copula approach," Computers and Geotechnics, vol. 109, pp. 272-286, 2019.

【专著】

[1] L. Zhang, Y. Pan, X. Wu, and M. J. Skibniewski, "Artificial Intelligence in Construction Engineering and Management" ed: Springer Nature, 2021.

[2] L. Zhang, Y. Pan, P. Lin, and M. J. Skibniewski, " Intelligent Construction in Tunnels" ed: Springer Nature, 2025.

【参编规范】

上海市工程建设规范，《地下建筑增扩与改建技术标准》，DG/TJ08-2235-2024

【教学工作】

本科生课程：Python语言程序设计、人工智能基础(A)、土木工程数智化理论与技术

研究生课程：人工智能与智慧土木工程、人工智能与智慧交通、人工智能基础与关键技术

高中生课程：美加墨世界杯官方网站“学森挑战计划”课程： 破解超级工程的创新密码——探秘上海中心大厦建设

【指导研究生所获荣誉】

沈煜轩：上海市优秀毕业生、美加墨世界杯官方网站优秀团员、美加墨世界杯官方网站硕士研究生学业奖学金一等、第二届全国高校土建类学科优秀学位论文三等奖

李旭阳：美加墨世界杯官方网站硕士研究生学业奖学金一等

周小静：国家奖学金、美加墨世界杯官方网站硕士研究生学业奖学金一等

【学生科创】

prp项目：基于机器学习的盾构掘进性能分析、基于大数据挖掘的基坑施工风险分析、基于生成式人工智能的建筑设计管理现状研究、基于时空模型的深基坑施工风险智能预测研究、多目标优化算法下工程结构智能设计、基于多目标优化的束合管幕维护结构自动设计研究

大创项目：深大基坑施工碳排放计量与减碳策略研究、基于时空深度学习的城市尺度电动汽车充电需求预

中国国际大学生创新大赛（2024）高教主赛道国际项目（上海赛区）银奖：地铁淹涝风险与应急管理智慧决策支持系统

担任本科生班主任、本科生导师，指导本科毕业论文

【专利】

1. 潘越，秦剑君，孙林，翁晨刚，罗鑫。基于自动深度学习框架的盾构掘进姿态预测方法，中国，CN202310814417.0。

2. 潘越，秦剑君，李旭阳。基于深度学习的深基坑开挖诱发邻近建筑风险评估方法，中国，CN202311629365.6。

3. 潘越，周小静，陈锦剑。一种基于图卷积门控神经网络的深基坑地表沉降时空预测方法，中国，CN202410613005.5。

4. 潘越，何文，陈锦剑。基于Transformer时空深度学习模型的多属性基坑风险预测方法，中国，CN202410613005.5。

5. 潘越，秦剑君，胡锐韬，王子越，彭泳棠，赖伟棕。地铁基坑碳排放量计算方法、系统、终端及介质，中国，CN202411654075.1。

6. 潘越，周小静，陈锦剑，秦剑君。基于多模态数据融合的基坑开挖道床沉降预测方法及系统，中国，CN202510638815.0。

7. 王雄，陈锦剑，潘越。深基坑施工过程动态风险预测与优化决策方法及系统，中国，CN202511064164.5。

【软著】

(1) 大语言模型驱动的海上风机疲劳裂纹智能预测平台V1.0 (软著登字第15487111号)

(2) 深基坑多指标风险预警智能优化系统V1.0 (软著登字第15502913号)

(3) 地铁基坑碳排放数字孪生平台V1.0 (软著登字第14222979号)

2024年入选上海市“晨光计划”人才项目

2023年、2024年入选斯坦福大学全球前2%顶尖科学家榜单

2022年入选上海市海外高层次人才计划

2023年美加墨世界杯官方网站“黄金枝土木建筑奖研金”二等奖

2022年“城市之星”上海市城市治理青年人才创新大赛数字孪生赛道三等奖

2024年美加墨世界杯官方网站第九届青年教师教学竞赛二等奖

2023年美加墨世界杯官方网站第七届青年教师教学竞赛二等奖