Structuring Visual Data in a Common Data Environment to Support Facilities’ Operations and Maintenance
Publication: Computing in Civil Engineering 2021
ABSTRACT
Image and video data can be valuable for supporting facilities’ operations and maintenance tasks. However, this data should be captured and cataloged during the construction phase of a project to enable use during the operations phase. With advancements in technology, there are many approaches to capture, store, and retrieve image- and video-based data. This research aims to review the existing methods to capture, tag, store, and retrieve such data along with how this data can be retrieved and updated to support operations and maintenance. First, a literature review was completed on the existing methods and technologies for image capture, storage, tagging, and retrieval. Afterward, a taxonomy was identified for image-based technology. Image capture can be performed by either fixed or mobile devices. Image tagging was defined as assigning information regarding the location and time of the image capture along with identified content within the image. This content can be identified either manually or automatically using computer vision techniques, such as object detection and image classification. A content analysis was conducted on common data environments for image storage. The findings of this research can be used by practitioners to support high-value use case implementation. Results can also be used to highlight areas where image-based technologies can be expanded through research and development initiatives.
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REFERENCES
Bunrit, S., Kerdprasop, N., and Kerdprasop, K. (2019). “Evaluating on the Transfer Learning of CNN Architectures to a Construction Material Image Classification Task.” Int. J. Mach. Learn. Comput., 9(2), 201–207.
Fang, W., Ding, L., Love, P. E. D., Luo, H., Li, H., Peña-Mora, F., Zhong, B., and Zhou, C. (2020). “Computer vision applications in construction safety assurance.” Autom. Constr., 110, 103013.
Golparvar-Fard, M., and Peña-Mora, F. (2007). “Application of Visualization Techniques for Construction Progress Monitoring.” IWCCE, ASCE, Pittsburgh, Pennsylvania, 216–223.
Ham, Y., and Kamari, M. (2019). “Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones.” Autom. Constr., 105, 102831.
ISO. (2018). ISO 19650 Concepts and principles, Organization and digitization of information about buildings and civil engineering works, including building information modelling (BIM) - Information management using building information modelling. International Standards Organization. 3.3.15.
ISO/IEC. (2009). ISO/IEC 26513: Systems and software engineering — Requirements for testers and reviewers of user documentation, 3.43.
Kamat, V. R., Martinez, J. C., Fischer, M., Golparvar-Fard, M., Peña-Mora, F., and Savarese, S. (2011). “Research in Visualization Techniques for Field Construction.” J. Constr. Eng., 137(10), 853–862.
Lin, J. J., Han, K. K., and Golparvar-Fard, M. (2015). “A Framework for Model-Driven Acquisition and Analytics of Visual Data Using UAVs for Automated Construction Progress Monitoring.” IWCCE, ASCE, Austin, Texas, 156–164.
Martinez, J. G., Albeaino, G., Gheisari, M., Issa, R. R. A., and Alarcón, L. F. (2021). “iSafeUAS: An unmanned aerial system for construction safety inspection.” Autom. Constr., 125, 103595.
Nath, N. D., Behzadan, A. H., and Paal, S. G. (2020). “Deep learning for site safety: Real-time detection of personal protective equipment.” Autom. Constr., 112, 103085.
Nikoohemat, S., Diakité, A., Zlatanova, S., and Vosselman, G. (2020). “Indoor 3D reconstruction from point clouds for optimal routing in complex buildings to support disaster management.” Autom. Constr., 113, 103109.
Perez-Perez, Y., Golparvar-Fard, M., and El-Rayes, K. (2020). “Convolutional Neural Network Architecture for Semantic Labeling Structural and Mechanical Elements.” CRC 2020: Computer Applications, Reston, VA: ASCE, 1336–1345.
Tang, S., Golparvar-Fard, M., Naphade, M., and Gopalakrishna, M. M. (2020). “Video-Based Motion Trajectory Forecasting Method for Proactive Construction Safety Monitoring Systems.” J. Comput. Civ. Eng., 34(6), 04020041.
Zaychenko, I., Smirnova, A., and Borremans, A. (2018). “Digital transformation: the case of the application of drones in construction.” MATEC Web Conf., 193, 05066.
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Published online: May 24, 2022
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