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http://dx.doi.org/10.6106/KJCEM.2020.21.5.011

Automated Construction Progress Management Using Computer Vision-based CNN Model and BIM  

Rho, Juhee (Department of Architectural Engineering, Seoul National University)
Park, Moonseo (Department of Architectural Engineering, Seoul National University)
Lee, Hyun-Soo (Department of Architectural Engineering, Seoul National University)
Publication Information
Korean Journal of Construction Engineering and Management / v.21, no.5, 2020 , pp. 11-19 More about this Journal
Abstract
A daily progress monitoring and further schedule management of a construction project have a significant impact on the construction manager's decision making in schedule change and controlling field operation. However, a current site monitoring method highly relies on the manually recorded daily-log book by the person in charge of the work. For this reason, it is difficult to take a detached view and sometimes human error such as omission of contents may occur. In order to resolve these problems, previous researches have developed automated site monitoring method with the object recognition-based visualization or BIM data creation. Despite of the research results along with the related technology development, there are limitations in application targeting the practical construction projects due to the constraints in the experimental methods that assume the fixed equipment at a specific location. To overcome these limitations, some smart devices carried by the field workers can be employed as a medium for data creation. Specifically, the extracted information from the site picture by object recognition technology of CNN model, and positional information by GIPS are applied to update 4D BIM data. A standard CNN model is developed and BIM data modification experiments are conducted with the collected data to validate the research suggestion. Based on the experimental results, it is confirmed that the methods and performance are applicable to the construction site management and further it is expected to contribute speedy and precise data creation with the application of automated progress monitoring methods.
Keywords
Construction Progress Monitoring; BIM; CNN Model; GIPS; Computer Vision;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Girshick, R., Donahue, H., Darrell, T., and Malik, J. (2014). "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation." In CVPR, pp. 1-22
2 Golparvar-fard, M., and Pena-Mora, F. (2007). "Application of Visualization Techniques for Construction Progress Monitoring." Congress on Computing in Civil Engineering, Proceedings, pp. 216-223.
3 Golparvar-fard, M., Heydarian, A., and Niebles, J. (2013). "Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers." Advanced Engineering Informatics, 27, pp. 652-663.   DOI
4 Hamledari, H., McCabe, B., and Davari, S. (2017). "Automated Computer Vision-Based Detection of Components of Under-Construction Indoor Partitions." Automation in Construction, 74, pp. 78-97.   DOI
5 Hamledari, H., McCabe, B., Davari, S., Shahi, A., Rezazadeh, E., and Flager, F. (2017). "Evaluation of Computer Vision-and 4D BIM-Based Construction Progress Tracking on a UAV Platform." Leadership in Sustainable Infrastructure, pp. 1-10.
6 Han, K., Lin, J., and Golparvar-fard, M. (2015). "A Formalism for Utilization of Autonomous Vision-Based Systems and Integrated Project Models for Construction Progress Monitoring." Proceedings of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure, pp. 118-131.
7 He, H., Zhang, X., Ren, S., and Sun, J. (2016). "Deep Residual Learning for Image Recognition." 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778.
8 He, K., Zhang, X., Ren, S., and Sun, J. (2015). "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition." In ECCV. pp. 1-14.
9 Hikodukue, K. Python Ni Yoru Scraping and Kinkaigakusho Kaihatsu Technique (2017). Socym Press, Japan.
10 Ibrahim, Y., Lukins, T., Zhang, X., Trucco, E., and Kaka, A. (2009). "Towards Automated Progress Assessment of Work package Components in Construction Projects Using Computer Vision." Advanced Engineering Informatics, 23, pp. 93-103.   DOI
11 BuildingSMART, (2018). "Industrial Foundation Classes from BuildingSMART." United States, (Accessed May 1, 2020)
12 Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). "Gradient-Based Learning Applied to Document Recognition." Proceedings of the IEEE, pp. 1-46.
13 Kim, S., Kim, Y., Yoou,, J., and Kim, E. (2012). "A framework of the open BIM-based integrated information system for the Korean Traditional House." Journal of Architectural Institute of Korea, 28(9), pp. 13-20.
14 Kong, J., and Jang, M. (2019). "Association Analysis of Convolution Layer, Kernel and Accuracy in CNN." Journal of the KIECS, 14(6), pp. 1153-1160.
15 Kropp, C., Koch, C., and Konig, M. (2018). "Interior construction state recognition with 4D BIM registered image sequences." Automation in Construction, 86, pp. 11-32.   DOI
16 McCulloch, C.E. (1997). "Maximum Likelihood Algorithms for Generalized Linear Mixed Models." Journal of the American Statistical Association, 92, pp. 162-170.   DOI
17 Memon, Z., Abd.Majid, M., and Mustaffar, M. (2005). "An Automatic Project Progress Monitoring Model by Integrating Auto CAD and Digital Photos." International Conference on Computing in Civil Engineering, pp. 1-13.
18 Redmon, J., Divvala, S., Girchick, R., and Fahadi, A. (2016). "You Only Look Once: Unified, Real-Time Object Detection." in CVPR, pp. 779-788.
19 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelow, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). "Going deeper with convolutions." in CVPR, pp. 1-9.
20 Simonyan, K., and Zisserman, A. (2015). "Very Deep Convolutional Networks for Large-Scale Image Recognition." International Conference on Learning Representations (ICLR), pp. 1-14.
21 Cho, T. (2018). Deep Learning for Everyone. Seoul: Gilbut Press.
22 Akhavia, R., and Behzadan, A. (2016). "Smartphone-based construction workers' activity recognition and classification." Automation in Construction, 71, pp. 198-209.   DOI
23 Azar, R. (2017). "Semantic Annotation of Videos from Equipment-Intensive Construction Operations by Shot Recognition and Probabilistic Reasoning." Journal of Computing in Civil Engineering, 31(5), pp. 04017042-04017042.   DOI
24 buildingSMART International Modeling Support Group (2009). "IFC 2x Edition 3 Model Implementation Guide." buildingSMART International Modeling Support Group.
25 Deng, H., Hong, H., Luo, D., Deng, Y., and Su, C. (2020). "Automatic Indoor Construction Process Monitoring for Tiles Based on BIM and Computer Vision." Journal of Construction Engineering and Management, 146(1), DOI: 10.1061/(ASCE)CO.1943-7862.0001744.
26 Eastman, C., Jeong, Y., Sack, R., and Karner, L. (2009). "Exchange model and exchange object concepts for implementation of national BIM standards." Journal of Computing in Civil Engineering, 24(1), pp. 24-35.
27 Son, H., Choi, H., Seong, H., and Kim, C. (2019). "Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks." Automation in Construction, 99, pp. 27-38.   DOI
28 Trucco, E., and Kaka, P. (2004). "A framework for automatic progress assessment on construction sites using computer vision." International Journal of IT in Architecture Engineering and Construction, 2(2), pp. 147-164.
29 Zhang, X., Bakis, N., Lukins, T., Ibrahim, Y., Wu, S., Kagioglou, M., Aouad, G., Kaka, A., and Trucco, E. (2009). "Automating Progress Measurement of Construction Projects." Automation in Construction, 18, pp. 294-301.   DOI
30 Ding, L., Fang, W., Luo, H., Love, P., Zhong, B., and Ouyang, X. (2018). "A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory." Automation in Construction, 86, pp. 118-124   DOI
31 FARO (2010). "FARO Scanner Production Technology." FARO Technologies, United States, (Accessed May 1, 2020)
32 Fang, W., Ding, L., Luo, H., and Love, D. (2018). "Falls from heights: A computer vision-based approach for safety harness detection." Automation in Construction, 91, pp. 53-61.   DOI