• Ghelmani A and Hammad A. (2024). Improving single‐stage activity recognition of excavators using knowledge distillation of temporal gradient data. Computer-Aided Civil and Infrastructure Engineering. 39:13. (2028-2053). Online publication date: 9-Jun-2024.

    https://doi.org/10.1111/mice.13157

  • Yang M, Wu C, Guo Y, He Y, Jiang R, Jiang J and Yang Z. (2024). A teacher–student deep learning strategy for extreme low resolution unsafe action recognition in construction projects. Advanced Engineering Informatics. 59:C. Online publication date: 1-Jan-2024.

    https://doi.org/10.1016/j.aei.2023.102294

  • Song H, Li G, He Z, Xiong X, He B and Mitrouchev P. Intelligent Identification Approach of Vibratory Roller Working Stages Based on Multi-dimensional Convolutional Neural Network. Intelligent Robotics and Applications. (463-475).

    https://doi.org/10.1007/978-981-99-6501-4_40

  • Cheng M, Cao M and Nuralim C. (2022). Computer vision-based deep learning for supervising excavator operations and measuring real-time earthwork productivity. The Journal of Supercomputing. 79:4. (4468-4492). Online publication date: 1-Mar-2023.

    https://doi.org/10.1007/s11227-022-04803-x

  • Jungmann M, Ungureanu L, Hartmann T, Posada H and Chacon R. Real-Time Activity Duration Extraction of Crane Works for Data-Driven Discrete Event Simulation. Proceedings of the Winter Simulation Conference. (2365-2376).

    /doi/10.5555/3586210.3586408

  • Wang Y, Xiao B, Bouferguene A, Al-Hussein M and Li H. (2022). Vision-based method for semantic information extraction in construction by integrating deep learning object detection and image captioning. Advanced Engineering Informatics. 53:C. Online publication date: 1-Aug-2022.

    https://doi.org/10.1016/j.aei.2022.101699

  • Liu W, Kang J, Xu W and Deng J. Optimization of excavator engine working points based on particle swarm algorithm. Proceedings of the Asia Conference on Electrical, Power and Computer Engineering. (1-8).

    https://doi.org/10.1145/3529299.3531508

  • Jung S, Jeoung J, Kang H and Hong T. (2021). 3D convolutional neural network‐based one‐stage model for real‐time action detection in video of construction equipment. Computer-Aided Civil and Infrastructure Engineering. 37:1. (126-142). Online publication date: 20-Dec-2021.

    https://doi.org/10.1111/mice.12695

  • Rashid K and Louis J. Automated active and idle time measurement in modular construction factory using inertial measurement unit and deep learning for dynamic simulation input. Proceedings of the Winter Simulation Conference. (1-8).

    /doi/10.5555/3522802.3522989

  • Ali D and Frimpong S. (2021). DeepHaul: a deep learning and reinforcement learning-based smart automation framework for dump trucks. Progress in Artificial Intelligence. 10:2. (157-180). Online publication date: 1-Jun-2021.

    https://doi.org/10.1007/s13748-021-00233-7

  • Wang M, Zhang J and Hou Y. Using Temporal Convolutional Networks to Enable Action Recognition for Construction Equipment. Proceedings of the 2020 4th International Conference on Video and Image Processing. (104-109).

    https://doi.org/10.1145/3447450.3447467

  • Ali D and Frimpong S. (2020). Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector. Artificial Intelligence Review. 53:8. (6025-6042). Online publication date: 1-Dec-2020.

    https://doi.org/10.1007/s10462-020-09841-6

  • Luo X, Li H, Yu Y, Zhou C and Cao D. (2020). Combining deep features and activity context to improve recognition of activities of workers in groups. Computer-Aided Civil and Infrastructure Engineering. 35:9. (965-978). Online publication date: 24-Aug-2020.

    https://doi.org/10.1111/mice.12538

  • Luo X, Li H, Yang X, Yu Y and Cao D. (2019). Capturing and Understanding Workers’ Activities in Far‐Field Surveillance Videos with Deep Action Recognition and Bayesian Nonparametric Learning. Computer-Aided Civil and Infrastructure Engineering. 34:4. (333-351). Online publication date: 6-Mar-2019.

    https://doi.org/10.1111/mice.12419

  • Yang X, Li H, Huang T, Zhai X, Wang F and Wang C. (2018). Computer‐Aided Optimization of Surveillance Cameras Placement on Construction Sites. Computer-Aided Civil and Infrastructure Engineering. 33:12. (1110-1126). Online publication date: 12-Nov-2018.

    https://doi.org/10.1111/mice.12385

  • Nath N, Shrestha P and Behzadan A. Human activity recognition and mobile sensing for construction simulation. Proceedings of the 2017 Winter Simulation Conference. (1-12).

    /doi/10.5555/3242181.3242389

  • Bügler M, Borrmann A, Ogunmakin G, Vela P and Teizer J. (2017). Fusion of Photogrammetry and Video Analysis for Productivity Assessment of Earthwork Processes. Computer-Aided Civil and Infrastructure Engineering. 32:2. (107-123). Online publication date: 1-Feb-2017.

    /doi/10.5555/3205242.3205245

  • Moshki M, Kabiri P and Mohebalhojeh A. (2017). Scalable data-driven modeling of spatio-temporal systems. Intelligent Data Analysis. 21:3. (577-595). Online publication date: 1-Jan-2017.

    https://doi.org/10.3233/IDA-150494

  • Yang J, Shi Z and Wu Z. (2016). Vision-based action recognition of construction workers using dense trajectories. Advanced Engineering Informatics. 30:3. (327-336). Online publication date: 1-Aug-2016.

    https://doi.org/10.1016/j.aei.2016.04.009

  • Akhavian R and Behzadan A. (2015). Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers. Advanced Engineering Informatics. 29:4. (867-877). Online publication date: 1-Oct-2015.

    https://doi.org/10.1016/j.aei.2015.03.001

  • Teizer J. (2015). Status quo and open challenges in vision-based sensing and tracking of temporary resources on infrastructure construction sites. Advanced Engineering Informatics. 29:2. (225-238). Online publication date: 1-Apr-2015.

    https://doi.org/10.1016/j.aei.2015.03.006

  • Seo J, Han S, Lee S and Kim H. (2015). Computer vision techniques for construction safety and health monitoring. Advanced Engineering Informatics. 29:2. (239-251). Online publication date: 1-Apr-2015.

    https://doi.org/10.1016/j.aei.2015.02.001

  • Yang J, Park M, Vela P and Golparvar-Fard M. (2015). Construction performance monitoring via still images, time-lapse photos, and video streams. Advanced Engineering Informatics. 29:2. (211-224). Online publication date: 1-Apr-2015.

    https://doi.org/10.1016/j.aei.2015.01.011

  • Akhavian R and Behzadan A. Construction activity recognition for simulation input modeling using machine learning classifiers. Proceedings of the 2014 Winter Simulation Conference. (3296-3307).

    /doi/10.5555/2693848.2694261

  • Kim Y, Lim H, Ahn S and Kim A. Simultaneous segmentation, estimation and analysis of articulated motion from dense point cloud sequence. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). (1085-1092).

    https://doi.org/10.1109/IROS.2016.7759184