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Die folgende Bibliografie enthält alle in dieser Datenbank indizierten Veröffentlichungen, die mit diesem Namen als Autor, Herausgeber oder anderweitig Beitragenden verbunden sind.

  1. Pan, Yue / Shen, Yuxuan / Qin, Jianjun / Zhang, Limao (2024): Deep reinforcement learning for multi-objective optimization in BIM-based green building design. In: Automation in Construction, v. 166 (Oktober 2024).

    https://doi.org/10.1016/j.autcon.2024.105598

  2. Ji, Ankang / Zhang, Limao / Fan, Hongqin / Xue, Xiaolong / Dou, Yudan (2023): Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds. In: Automation in Construction, v. 156 (Dezember 2023).

    https://doi.org/10.1016/j.autcon.2023.105131

  3. Zhang, Limao / Guo, Jing / Fu, Xianlei / Tiong, Robert Lee Kong / Zhang, Penghui (2024): Digital twin enabled real-time advanced control of TBM operation using deep learning methods. In: Automation in Construction, v. 158 (Februar 2024).

    https://doi.org/10.1016/j.autcon.2023.105240

  4. Zhang, Zhaoxiang / Ji, Ankang / Zhang, Limao / Xu, Yuelei / Zhou, Qing (2023): Deep learning for large-scale point cloud segmentation in tunnels considering causal inference. In: Automation in Construction, v. 152 (August 2023).

    https://doi.org/10.1016/j.autcon.2023.104915

  5. Wang, Kunyu / Wu, Xianguo / Li, Heng / Wang, Fan / Zhang, Limao / Chen, Hongyu (2024): Adaptively unsupervised seepage detection in tunnels from 3D point clouds. In: Structure and Infrastructure Engineering, v. 20, n. 9 (Januar 2024).

    https://doi.org/10.1080/15732479.2022.2136718

  6. Zhou, Yunxiang / Ji, Ankang / Zhang, Limao / Xue, Xiaolong (2023): Attention-enhanced sampling point cloud network (ASPCNet) for efficient 3D tunnel semantic segmentation. In: Automation in Construction, v. 146 (Februar 2023).

    https://doi.org/10.1016/j.autcon.2022.104667

  7. Fu, Xianlei / Wu, Maozhi / Tiong, Robert Lee Kong / Zhang, Limao (2023): Data-driven real-time advanced geological prediction in tunnel construction using a hybrid deep learning approach. In: Automation in Construction, v. 146 (Februar 2023).

    https://doi.org/10.1016/j.autcon.2022.104672

  8. Luo, Shan / Wang, Tao / Zhang, Limao / Liu, Bingsheng (2022): Decision-Making Based on Network Analyses of New Infrastructure Layouts. In: Buildings, v. 12, n. 7 (5 Juli 2022).

    https://doi.org/10.3390/buildings12070937

  9. Pan, Yue / Fu, Xianlei / Zhang, Limao (2022): Data-driven multi-output prediction for TBM performance during tunnel excavation: An attention-based graph convolutional network approach. In: Automation in Construction, v. 141 (September 2022).

    https://doi.org/10.1016/j.autcon.2022.104386

  10. Wang, Ying / Chew, Alvin Wei Ze / Zhang, Limao (2022): Building damage detection from satellite images after natural disasters on extremely imbalanced datasets. In: Automation in Construction, v. 140 (August 2022).

    https://doi.org/10.1016/j.autcon.2022.104328

  11. Chew, Alvin Wei Ze / Zhang, Limao (2022): Data-Driven Multiscale Modelling and Analysis of COVID-19 Spatiotemporal Evolution Using Explainable AI. In: Sustainable Cities and Society, v. 80 (Mai 2022).

    https://doi.org/10.1016/j.scs.2022.103772

  12. Pan, Yue / Zhang, Limao / Unwin, Juliette / Skibniewski, Miroslaw J. (2022): Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States. In: Sustainable Cities and Society, v. 77 (Februar 2022).

    https://doi.org/10.1016/j.scs.2021.103508

  13. Zhang, Gaowei / Pan, Yue / Zhang, Limao (2022): Deep learning for detecting building façade elements from images considering prior knowledge. In: Automation in Construction, v. 133 (Januar 2022).

    https://doi.org/10.1016/j.autcon.2021.104016

  14. Chew, Alvin Wei Ze / Wang, Ying / Zhang, Limao (2021): Correlating dynamic climate conditions and socioeconomic-governmental factors to spatiotemporal spread of COVID-19 via semantic segmentation deep learning analysis. In: Sustainable Cities and Society, v. 75 (Dezember 2021).

    https://doi.org/10.1016/j.scs.2021.103231

  15. Pan, Yue / Zhang, Limao / Yan, Zhenzhen / Lwin, May O. / Skibniewski, Miroslaw J. (2021): Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia. In: Sustainable Cities and Society, v. 75 (Dezember 2021).

    https://doi.org/10.1016/j.scs.2021.103254

  16. Pan, Yue / Zhang, Limao (2021): Automated process discovery from event logs in BIM construction projects. In: Automation in Construction, v. 127 (Juli 2021).

    https://doi.org/10.1016/j.autcon.2021.103713

  17. Tan, Yi / Zhang, Limao (2019): Computational methodologies for optimal sensor placement in structural health monitoring: A review. In: Structural Health Monitoring, v. 19, n. 4 (September 2019).

    https://doi.org/10.1177/1475921719877579

  18. Pan, Yue / Zhang, Limao (2021): A BIM-data mining integrated digital twin framework for advanced project management. In: Automation in Construction, v. 124 (April 2021).

    https://doi.org/10.1016/j.autcon.2021.103564

  19. Guo, Kai / Zhang, Limao / Wang, Tao (2020): Concession period optimisation in complex projects under uncertainty: a public–private partnership perspective. In: Construction Management and Economics, v. 39, n. 2 (Oktober 2020).

    https://doi.org/10.1080/01446193.2020.1849752

  20. Pan, Yue / Zhang, Limao / Skibniewski, Miroslaw J. (2020): Clustering of designers based on building information modeling event logs. In: Computer-Aided Civil and Infrastructure Engineering, v. 35, n. 7 (April 2020).

    https://doi.org/10.1111/mice.12551

  21. Wang, Yongqi / Thangasamy, Vimal Kumar / Hou, Zhaoqi / Tiong, Robert L. K. / Zhang, Limao (2020): Collaborative relationship discovery in BIM project delivery: A social network analysis approach. In: Automation in Construction, v. 114 (Juni 2020).

    https://doi.org/10.1016/j.autcon.2020.103147

  22. Pan, Yue / Zhang, Limao (2020): BIM log mining: Learning and predicting design commands. In: Automation in Construction, v. 112 (April 2020).

    https://doi.org/10.1016/j.autcon.2020.103107

  23. Luo, Lan / Zhang, Limao / Wu, Guangdong (2020): Bayesian Belief Network-Based Project Complexity Measurement Considering Causal Relationships. In: Journal of Civil Engineering and Management, v. 26, n. 2 (Februar 2020).

    https://doi.org/10.3846/jcem.2020.11930

  24. Pan, Yue / Zhang, Limao (2020): BIM log mining: Exploring design productivity characteristics. In: Automation in Construction, v. 109 (Januar 2020).

    https://doi.org/10.1016/j.autcon.2019.102997

  25. Zhang, Limao / Wu, Xianguo / Ding, Lieyun / Skibniewski, Miroslaw J. / Lu, Yujie (2016): BIM-based Risk Identification System in Tunnel Construction. In: Journal of Civil Engineering and Management, v. 22, n. 4 (August 2016).

    https://doi.org/10.3846/13923730.2015.1023348

  26. Wang, Tao / Li, Yulong / Zhang, Limao / Li, Guijun (2016): Case Study of Integrated Prefab Accommodations System for Migrant On-Site Construction Workers in China. In: Journal of Professional Issues in Engineering Education and Practice, v. 142, n. 4 (Oktober 2016).

    https://doi.org/10.1061/(asce)ei.1943-5541.0000288

  27. Zhang, Limao / Wen, Ming / Ashuri, Baabak (2018): BIM Log Mining: Measuring Design Productivity. In: Journal of Computing in Civil Engineering, v. 32, n. 1 (Januar 2018).

    https://doi.org/10.1061/(asce)cp.1943-5487.0000721

  28. Zhang, Limao / Wu, Xianguo / Liu, Menjie / Liu, Wenli / Ashuri, Baabak (2019): Discovering worst fire scenarios in subway stations: A simulation approach. In: Automation in Construction, v. 99 (März 2019).

    https://doi.org/10.1016/j.autcon.2018.12.007

  29. Zhang, Limao / Wu, Xianguo / Skibniewski, Miroslaw J. / Fang, Weili / Deng, Qianli (2015): Conservation of historical buildings in tunneling environments: Case study of Wuhan metro construction in China. In: Construction and Building Materials, v. 82 (Mai 2015).

    https://doi.org/10.1016/j.conbuildmat.2015.02.031

  30. Zhang, Limao / Ashuri, Baabak (2018): BIM log mining: Discovering social networks. In: Automation in Construction, v. 91 (Juli 2018).

    https://doi.org/10.1016/j.autcon.2018.03.009

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