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http://dx.doi.org/10.13161/kibim.2020.10.4.060

Augmented Reality Framework for Efficient Access to Schedule Information on Construction Sites  

Lee, Yong-Ju (명지대학교 토목환경공학과)
Kim, Jin-Young (명지대학교 토목환경공학과)
Pham, Hung (명지대학교 토목환경공학과)
Park, Man-Woo (명지대학교 토목환경공학과)
Publication Information
Journal of KIBIM / v.10, no.4, 2020 , pp. 60-69 More about this Journal
Abstract
Allowing on-site workers to access information of the construction process can enable task control, data integration, material and resource control. However, in the current practice of the construction industry, the existing methods and scope is quite limited, leading to inefficient management during the construction process. In this research, by adopting cutting edge technologies such as Augmented Reality(AR), digital twins, deep learning and computer vision with wearable AR devices, the authors proposed an AR visualization framework made of virtual components to help on-site workers to obtain information of the construction process with ease of use. Also, this paper investigates wearable AR devices and object detection algorithms, which are critical factors in the proposed framework, to test their suitability.
Keywords
Digital twin; Augmented reality; Computer vision; Deep learning; Wearable device; Schedule;
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1 Trimble (2019). Trimble connect for HoloLens, https://mixedreality.trimble.com (Dec. 28. 2020).
2 Tuegel, E. J., Ingraffea, A. R., Eason, T. G., Spottswood, S. M. (2011). Reengineering Aircraft Structural Life Prediction Using a Digital Twin. International Journal of Aerospace Engineering, 2011, doi:10.1155/2011/154798   DOI
3 Azuma, R. T. (1997). A Survey of Augmented Reality. PRESENCE: Virtual and Augmented Reality, 6(4), pp. 355-385.   DOI
4 ART+COM STUDIOS (2013). Augmented reality, https://artcom.de/en/?resea rch_focus=augmented-reality (Dec. 28. 2020).
5 Azuma, R., Lee, J. W., Jiang, B., Park, J., You, S., Neumann, U. (1999). Tracking in unprepared environments for augmented reality systems. Computers & Graphics, 23, pp. 787-793. https://doi.org/10.1016/S0097-8493(99)00104-1   DOI
6 Barricelli, B. R., Casiraghi, E., Fogli, D. (2019). A Survey on Digital Twin: Definitions, Characteristics, Applications and Design Implications. IEEE Access, 7, 167653-167671, doi: 10.1109/ACCESS.2019.2953499   DOI
7 Chatzopoulos, D., Bermejo, C., Huang, Z., Hui, P. (2017). Mobile Augmented Reality Survey: From Where We Are to Where We go. IEEE Access, 5, pp. 6917-6950.   DOI
8 Dunston, P. S., Wang, X. (2005). Mixed Reality-Based Visualization Interfaces for Architecture, Engineering, and Construction Industry. Journal of Construction Engineering & Management, 131(12), pp. 1301-1309.   DOI
9 Hanna, M. G., Ahmed, I., Nine, J., Shyam, P., Pantanowitz, L. (2018). Augmented Reality Technology Using Microsoft HoloLens in Anatomic Pathology. Archives of Pathology & Laboratory Medicine, 142, pp. 638-644.   DOI
10 Hochhalter, J. D., Leser, W. P., Newman, J. A., Glaessgen, E. H., Gupta, V. K., Yamakov, V., Cornell, S. R., Willard, S. A., Heber, G. (2014). Coupling Damage-Sensing Particles to the Digitial Twin Concept. NASA Center for AeroSpace Information
11 Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., Holmstrom, J. (2019). Digital Twin: Vision, Benefits, Boundaries, and Creation for Buildings. IEEE Access 7, 147406-147419. https://doi.org/10.1109/ACCESS.2019.2946515   DOI
12 Li, X., Yi, W., Chi, H. L., Wang, X., Chan, A. P. C. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86, pp.150-162.   DOI
13 Lim, K. Y. H., Pai, Z., Chen, C. H. (2020). A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31, pp. 1313-1337. https://doi.org/10.1007/s10845-019-01512-w   DOI
14 Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikinen, M. (2020). Deep learning for generic object detection: A survey. International journal of computer vision, 128(2), pp. 261-318.   DOI
15 Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., Berg, A. C. (2016). Ssd: Single shot multibox detector. In European conference on computer vision, pp. 21-37.
16 Redmon, J., Farhadi, A. (2018). Yolov3: An incremental improvement, https://arxiv.org/abs/1804.02767 (Sep. 03. 2020).
17 Ren, S., He, K., Girshick, R., Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pp. 91-99.
18 Robert, G. W., Evans, A., Dodson, A. H., Denby, B., Cooper, S., Hollands, R. (2002). The Use of Augmented Reality, GPS and INS for Subsurface Data Visualisation. FIG XXII International Congress, Washington, D.C. USA, April 19-26 2002
19 Souza, E. (2019). 9 Augmented reality technologies for architecture and construction, https://www.archdaily.com/914501/9-augmented-reality-technologies-forarchitecture-and-construction (Dec. 28. 2020).
20 Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94, pp. 3563-3576. https://doi.org/10.1007/s00170-017-0233-1   DOI