土木工程系
电子邮件：panyue001@sjtu.edu.cn
通讯地址：上海市闵行区东川路800号威尼斯9499登录入口木兰船建大楼A401室
【工作经历】
2021.10 -至今：威尼斯9499登录入口，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土木工程全生命周期管理中的理论发展和实践应用。
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 Sciences
Information Fusion
Expert Systems with Applications
Engineering Applications of Artificial Intelligence
Reliability Engineering and System Safety
Knowledge-based System
中国图学学会建筑信息模型（BIM）专业委员会委员
国家重点研发计划项目子课题，2023YFC3009402-05，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, 主持
国家自然科学基金委员会, 青年科学基金项目, 72201171, 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
上海市科学技术委员会, 2022年度上海市启明星项目（扬帆专项）, 22YF1419100, 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
建筑能效控制与评估教育部工程研究中心, 地方重点实验室开放基金, AHJZNX-2023-03, 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
智能建筑与建筑节能安徽省重点实验室, 地方重点实验室开放基金, IBES2021KF06, 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
浙江省海洋岩土工程与材料重点实验室，地方重点实验室开放基金，OGME22006，海上风电机组系统智慧运维数字孪生构建，2023-01 至 2024-12, 主持
上海市公共建筑和基础设施数字化运维重点实验室，地方重点实验室开放基金，AF0100120/006，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
威尼斯9499登录入口“双一流”建设项目人才科研启动基金，WH220501005，2021.10-2024.9，主持
上海勘察设计研究院（集团）有限公司，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
国家自然科学基金委员会, 重点项目,季节性气候影响下膨胀土工程边坡的失稳机制与防控理论，2024-01至2028-12，参与
截止2024年3月，在基于人工智能的土木工程建造与运维管理研究领域已积累较多的前期研究成果，已发表SCI论文40余篇，含第一作者论文29篇，通讯作者论文5篇，6篇入选ESI高被引论文，1篇入选ESI热点论文，总引用次数达3000余次，H-index值为25，参编英文专著1部，参编上海市规范1部。
【一作或通讯SCI论文】
[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, 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, 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, 2023.
[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, 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 333 (2023) 120575
[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 326 (2022) 119938
[23] Y. Pan, L. Zhang, Modeling and analyzing dynamic social networks for behavioral pattern discovery in collaborative design, Advanced Engineering Informatics 54 (2022) 101758
[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, L. Zhang, Dual attention deep learning network for automatic steel surface defect segmentation, Computer‐Aided Civil and Infrastructure Engineering 37 (11) (2022) 1468-1487
[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, 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.
[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, 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, 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, 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, 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.
【国际会议论文】
Y. Pan and L. Zhang, "Exploring Collaborative Networks in BIM Design Based on Event Logs.", Construction Research Congress 2020: Computer Applications, American Society of Civil Engineers Reston, VA, 2020, pp. 306-315.
Zhang, C., Y. Pan*, Y. Hou and J. Chen An Intelligent Model Mixing Physics Mechanism and Field Data for Ground Settlement Prediction during Pipe-Roofing Excavation. Geo-Risk 2023: 200-210.
【专著】
L. Zhang, Y. Pan, X. Wu, and M. J. Skibniewski, "Artificial Intelligence in Construction Engineering and Management," ed: Springer, 2021.
本科生课程：
《Python语言程序设计》
《土木工程学导论》
研究生课程：
《人工智能基础及关键技术》
《人工智能与智慧交通》
高中生课程：
《威尼斯9499登录入口“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》
2023年威尼斯9499登录入口第七届青年教师教学竞赛二等奖
入选斯坦福大学全球前2%顶尖科学家榜单
2023年威尼斯9499登录入口“黄金枝土木建筑奖研金”二等奖
2022年入选上海市海外高层次人才计划
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
2021年度国家优秀留学生奖学金B类
2018年-2021年 新加坡南洋理工大学全额奖学金
2016年-2017年 美国卡内基梅隆大学土木与环境工程学院奖学金
2016年 同济大学优秀毕业生
2015年 同济大学优秀学生
