Abstract
On-site progress monitoring is essential for keeping track of the ongoing work on construction sites. Currently, this task is a manual, time-consuming activity. BIM-based progress monitoring facilitates the automated comparison of the actual state of construction with the planned state for the early detection of deviations in the construction process. In this chapter, we discuss an approach where the actual state of the construction site is captured using photogrammetric surveys. From these recordings, dense point clouds are generated by the fusion of disparity maps created with semi-global-matching (SGM). These are matched against the target state provided by a 4D Building Information Model. For matching the point cloud and the model, the distances between individual points of the cloud and a component’s surface are aggregated using a regular cell grid. For each cell, the degree of coverage is determined. Based on this, a confidence value is computed which serves as a basis for detecting the existence of a respective component. Additionally, process- and dependency-relations provided by the BIM model are taken into account to further enhance the detection process.
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Bosché, F. (2012). Plane-based registration of construction laser scans with 3D/4D building models. Advanced Engineering Informatics, 26(1), 90–102.
Bosché, F., & Haas, C. T. (2008). Automated retrieval of project three-dimensional CAD objects in range point clouds to support automated dimensional QA/QC. Information Technologies in Construction, 13, 71–85.
Braun, A., Tuttas, S., Stilla, U., & Borrmann, A. (2014). Towards automated construction progress monitoring using BIM-based point cloud processing. In eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2014.
Braun, A., Tuttas, S., Borrmann, A., & Stilla, U. (2015a). A concept for automated construction progress monitoring using BIM-based geometric constraints and photogrammetric point clouds. Information Technologies in Construction, 20, 68–79.
Braun, A., Tuttas, S., Borrmann, A., & Stilla, U. (2015b). Automated progress monitoring based on photogrammetric point clouds and precedence relationship graphs. In Proceedings of the 32nd International Symposium on Automation and Robotics in Construction and Mining (pp. 274–280).
Brilakis, I., Fathi, H., & Rashidi, A. (2011a) Progressive 3D reconstruction of infrastructure with videogrammetry. Automation in Construction, 20(7), pp. 884–895.
Brilakis, I., German, S., & Zhu, Z. (2011b). Visual pattern recognition models for remote sensing of civil infrastructure. Journal of Computing in Civil Engineering, 25(5), 388–393.
Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2011a). Integrated sequential as-built and as-planned representation with tools in support of decision-making tasks in the AEC/FM industry. Journal of Construction Engineering and Management, 137(12), 1099–1116.
Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2011b). Monitoring changes of 3D building elements from unordered photo collections. In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) (pp. 249–256).
Golparvar-Fard, M., Peña Mora, F., & Savarese, S. (2015). Automated progress monitoring using unordered daily construction photographs and IFC based building information models. Journal of Computing in Civil Engineering, 29(1), 04014025.
Huhnt, W. (2005). Generating sequences of construction tasks. In CIB-W78’s 22nd International Conference on Information Technology in Construction, Dresden
Ibrahim, Y., Lukins, T., Zhang, X., Trucco, E., & Kaka, A. P. (2009). Towards automated progress assessment of workpackage components in construction projects using computer vision. Advanced Engineering Informatics, 23(1), 93–103.
Kim, C., Son, H., & Kim, C. (2013a). Automated construction progress measurement using a 4D building information model and 3D data. Automation in Construction, 31, 75–82.
Kim, C., Son, H., & Kim, C. (2013b). Fully automated registration of 3D data to a 3D CAD model for project progress monitoring. Automation in Construction, 35, 587–594.
Kropp, C., Koch, C., & König, M. (2018). Interior construction state recognition with 4D BIM registered image sequences. Automation in Construction, 86, 11–32.
Omar, T., & Nehdi, M. L. (2016). Data acquisition technologies for construction progress tracking. Automation in Construction, 70, 143–155.
Rankohi, S., & Waugh, L. (2014). Image-based modeling approaches for projects status comparison. In CSCE 2014 General Conference (Rankohi 2013) (pp. 1–10). Retrieved from http://lasso.its.unb.ca/publications/Sara_Rankohi_Lloyd_Waugh_CSCE_2014_Generalconference.pdf
Rashidi, A., Brilakis, I., & Vela, P. (2015). Generating absolute-scale point cloud data of built infrastructure scenes using a monocular camera setting. Journal of Computing in Civil Engineering, 29(6), 04014089.
Son, H., & Kim, C. (2010). 3D structural component recognition and modeling method using color and 3D data for construction progress monitoring. Automation in Construction, 19(7), 844–854.
Turkan, Y. (2012). Automated Construction Progress Tracking Using 3D Sensing Technologies. Dissertation, University of Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/6628
Turkan, Y., Bosché, F., Haas, C. T., & Haas, R. (2011). Automated progress tracking of erection of concrete structures. In Annual Conference of the Canadian Society for Civil Engineering (pp. 2746–2756). Retrieved from http://web.sbe.hw.ac.uk/fbosche/publications/conference/Turkan-2011-CSCE.pdf
Tuttas, S., Braun, A., Borrmann, A., & Stilla, U. (2014). Comparison of photogrammetric point clouds with BIM building elements for construction progress monitoring. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. XL-3).
Tuttas, S., Braun, A., Borrmann, A., & Stilla, U. (2016). Evaluation of acquisition strategies for image-based construction site monitoring. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Prague, ISPRS Congress.
Zhang, C., & Arditi, D. (2013). Automated progress control using laser scanning technology. Automation in Construction, 36, 108–116.
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Braun, A., Tuttas, S., Stilla, U., Borrmann, A. (2018). BIM-Based Progress Monitoring. In: Borrmann, A., König, M., Koch, C., Beetz, J. (eds) Building Information Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-92862-3_28
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DOI: https://doi.org/10.1007/978-3-319-92862-3_28
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