A vision-based excavator productivity analysis in Vietnam
Email:
huyvuquang@utc.edu.vn
Từ khóa:
Excavator, productivity analysis, technology, video interpretation, visual tracking, visual basic application
Tóm tắt
The process of determining the working parameters of reverse bucket excavators is mainly consulted through the Ministry of Construction norm. However, in the era of industrialization and modernization, machine and equipment are increasingly modern and innovative, making the determination of excavator productivity or parameters through the regulations in the old norms unsuitable. Furthermore, updating the norms through data collected in the field take tremendous amount of time and procedures as it is labor intensive. Therefore, this paper proposes a vision-based analysis in calculating excavator productivity using image processing applications and coding language to automatically determine the excavator productivity and bring results on the basis of analysing big data collected from validated construction sites. To be specific, this paper introduces a new method in calculating the excavator productivity by extracting crucial coefficients from hundred images of the excavators using an open-source software, then compare with the traditional method to identify and analyse the importance of this new method and the practical use it might bring to the construction industry.Tài liệu tham khảo
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[2] H. Kim et al., Application of dynamic time warping to the recognition of mixed equipment activities in cycle time measurement, Automation in Construction, 87 (2018) 225-234. https://doi.org/10.1016/j.autcon.2017.12.014
[3] J. Gong, C. H. Caldas, Computer vision-based video interpretation model for automated productivity analysis of construction operations, Journal of Computing Civil Engineering, 24 (2010) 252-263. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000027
[4] J. J. Adrian, L. T. Boyer, Modeling method-productivity, Journal of the Construction Division, 102 (1976) 34-37. https://cedb.asce.org/CEDBsearch/record.jsp?dockey=0006550
[5] C. H. Oglesby, H. W. Parker, G. A. Howell, Productivity improvement in construction, Mcgraw Hill Series In Construction Engineering And Project Management, 1989. https://www.biblio.com/9780070478022
[6] Y. Y. Su, L. Y. Liu, A case study of monitoring construction operations using automated position tracking system, Civil and Environmental Engineering, 19 (2006) 312–317. https://experts.illinois.edu/en/publications/
[7] J. Abeid et al., Photo-net II: a computer-based monitoring system applied to project management, Automation in Construction, 12 (2003) 603-616. https://doi.org/10.1016/S0926-5805(03)00042-6
[8] J. Yang et al., Vision-based tower crane tracking for understanding construction activity, Journal of Computing in Civil Engineering, 28 (2014) 103-112. http://doi.org/10.1061/(ASCE)CP.1943-5487.0000242
[9] J. Kim, S. Chi, J. Seo, Interaction analysis for vision-based activity identification of earthmoving excavators and dump trucks, Automation in Construction, 87 (2018) 294-312. https://doi.org/10.1016/j.autcon.2017.12.016
[10] J. Yang, Z. Shi, Z. Wu, Vision-based action recognition of construction workers using dense trajectories, Advanced Engineering Informatics, 30 (2016) 327-336. https://doi.org/10. 1016/j.aei.2016.04.009
[11] G. Ofori, Construction in developing countries, Construction management and economics, 25 (2007) 1-6. https://doi.org/10.1080/01446190601114134
[12] J. Park, D. Ryu, K. Lee, What determines the economic size of a nation in the world: Determinants of a nation’s share in world GDP vs. per capita GDP, Structure change and economic dynamics, 51 (2019) 203-214. https://doi.org/10.1016/j.strueco .2019.09.001
[13] S. Knack, P. Keefer, Institutions and economic performance: Cross-country tests using alternative institutional measures, Economics and politics, 7 (1995) 207–227. https://doi.org/10.1111/j.1468-0343.1995.tb00111.x
[14] J.W.Sun, Energy intensity versus per capita GDP in seven developing countries, Fuel and Energy Abstracts, 46 (2005) 66-67. https://doi.org/10.1016/S0140-6701(05)80509-0
[15] R. Akhavian, A. H. Behzadan, Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers, Advanced Engineering Informatics, 29 (2015) 867-877. https://doi.org/10.1016/j.aei.2015.03.001
[16] N. Pradhananga, J. Teizer, Automatic spatio-temporal analysis of construction site equipment operations using GPS data, Automation in Construction, 29 (2013) 107-122. https://doi.org/10.1016/j.autcon.2012.09.004
[17] P. Goodrum, M. McLaren, A. Durfee, The application of active radio frequency identification technology for tool tracking on construction job sites, Automation in Construction, 15 (2006) 292-302. https://doi.org/10.1016/j.autcon.2005.06.004
[18] D. Grau et al., Assessing the impact of materials tracking technologies on construction craft productivity, Automation in Construction, 18 (2009) 903-911. https://doi.org/10.1016/j.autcon.2009.04.001
[19] E. J. Jaselskis, T. El-Misalami, Implementing radio frequency identification in the construction process, Journal of Construction Engineering and Management, 129 (2003) 680-688. https://ascelibrary.org/doi/10.1061/(ASCE)0733-9364(2003)129:6(680)
[20] I. Niskanen et al., 4D modeling of soil surface during excavation using a solid-state 2D profilometer mounted on the arm of an excavator, Automation in Construction, 112 (2020) 43-46. https://doi.org/10.1016/j.autcon.2020.103112
[21] J. Kim, D. Lee, J. Seo, Task planning strategy and path similarity analysis for an autonomous excavator, Automation in Construction, 112 (2020) 53-57. https://doi.org/10.1016/ j.autcon.2020.103108
[22] Y. Shi et al., Intelligent identification for working-cycle stages of excavator based on main pump pressure, Automation in Construction, 109 (2020) 23-26. https://doi.org/10.1016/j.autcon.2019.102991
[23] S.W. Nunnally, Construction methods and management, Pentice Hall US, (1998) 45–52. https://www.civilengineerspk.com/construction-methods-management-s-w-nunnally/
[24] N. D. Thuan, Using Construction Machine for constructing road, Vietnam transportation publisher, 2001. https://www.vinabook.com/su-dung-may-xay-dung-va-lam-duong-p9250.html
[25] SAE standard, Excavator, Mini-excavator, and Backhoe Hoe Bucket Volumetric Rating, SAE International, 1999. https://www.sae.org/standards/content/j296_199901/
[26] B. Pueo, A. Tomas, J. Olmedo, Validity, reliability and usefulness of smartphone and kinovea motion analysis software for direct measurement of vertical jump height, Physiology & Behavior, 227 (2020) 41-44. https://doi.org/10.1016/j.physbeh.2020.113144
[27] A. Divi et al., Validity and reliability of the Kinovea program in obtaining angles and distances using coordinates in 4 perspectives, Plos One Journal, 14 (2019) 1-14. https://doi.org/10.1371/journal.pone.0216448
[28] N. Adnan et al., Biomechanical analysis using Kinovea for sports application, IOP Conference Series Materials Science and Engineering, 342 (2018) 012097. http://doi.org/10.1088/1757-899X/342/1/012097
[29] M. Sipos, R. Sweeney, Behavioral data management using visual basic for applications to automate data capture and analysis. Journal of Neuroscience Methods, 128 (2003) 53-65. https://doi.org/10.1016/S0165-0270(03)00150-X
[30] B. Bertheussen, Power to business professors: Automatic grading of problem-solving tasks in a spreadsheet, Journal of Accounting Education, 32 (2014) 76-87. https://doi.org/10.1016/j.jaccedu.2014.01.002
[31] K. Hyde, H. Maier, Distance-based and stochastic uncertainty analysis for multi-criteria decision analysis in Excel using Visual Basic for Applications, Environmental Modelling & Software, 21 (2006) 1695-1710. https://doi.org/10.1016/j.envsoft.2005.08.004
[32] S. Ozkaya, Superpose-An excel visual basic program for fracture modeling based on the stress superposition method, Computers & Geosciences, 64 (2014) 41–51. https://doi.org/10.1016/j.cageo.2013.11.011
[33] S. Snedecor et al., Comparative Computation Speed of Excel, Visual Basic for Applications, R, and Java in Execution of a Microsimulation Model With Probabilistic Sensitivity Analysis, Value in Health, 15 (2012) 472. https://doi.org/10.1016/j.jval.2012.08.1531
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Nhận bài
04/11/2020
Nhận bài sửa
17/12/2020
Chấp nhận đăng
23/12/2020
Xuất bản
27/05/2021
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Kiểu trích dẫn
Vu Quang, H., & Nguyen Hoang, T. (1622048400). A vision-based excavator productivity analysis in Vietnam. Tạp Chí Khoa Học Giao Thông Vận Tải, 72(4), 423-436. https://doi.org/10.47869/tcsj.72.4.3
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