Large-Scale Visual Data–Driven Probabilistic Risk Assessment of Utility Poles Regarding the Vulnerability of Power Distribution Infrastructure Systems
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 10
Abstract
Inspecting and assessing existing utility poles has become increasingly important for reducing the vulnerability of power distribution infrastructure systems in disaster situations, which can enhance community resilience. Although vision-based systems have been applied to detect faults in power distribution infrastructures, little research currently exists on assessing component- and network-level failures of utility poles based on their geometric and environmental information. This paper aims to propose a new data-driven approach to support risk-informed decision-making for utility maintenance under extreme wind conditions. Large-scale open-source imagery from Google Street View is used to assess geometric properties of utility poles (i.e., leaning angle). Then the failure probability of utility poles is analyzed under varying conditions (e.g., age, leaning angle, and wind loads) in a three-dimensional virtual city model. The proposed method is tested through case studies in Texas to (1) validate an algorithm for estimating leaning angles of utility poles and (2) understand the progress of failures of leaning utility poles from a network perspective. The outcomes of the case studies demonstrate that the proposed method has the potential to leverage large-scale open-source visual data to assess the vulnerability of utility pole networks that may lead to cascading failures in power distribution infrastructure systems. Based on the proposed virtual environment, the method is expected to enable practitioners to facilitate risk-informed decision-making against disaster situations, which creates an opportunity for prioritizing maintenance tasks regarding power distribution infrastructures.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request (e.g., visual data of utility poles).
References
Adabo, G. J. 2014. “Long range unmanned aircraft system for power line inspection of Brazilian electrical system.” J. Energy Power Eng. 8 (2): 394–398. https://doi.org/10.17265/1934-8975/2014.02.025.
AEP (American Electric Power) Texas. 2017. “AEP Texas Hurricane Harvey restoration.” Accessed December 31, 2019. https://www.aeptexas.com/info/news/viewRelease.aspx?releaseID=2339.
Alam, M., B. Eren, and T. Seokyon. 2019. “Framework for measuring the resilience of utility poles of an electric power distribution network.” Int. J. Disaster Risk Sci. 10 (2): 270–281. https://doi.org/10.1007/s13753-019-0219-8.
Alam, M. M., Z. Zhu, B. Eren Tokgoz, J. Zhang, and S. Hwang. 2020. “Automatic assessment and prediction of the resilience of utility poles using unmanned aerial vehicles and computer vision techniques.” Int. J. Disaster Risk Sci. 11 (1): 119–132. https://doi.org/10.1007/s13753-020-00254-1.
Apte, J. S., K. P. Messier, S. Gani, M. Brauer, T. W. Kirchstetter, M. M. Lunden, J. D. Marshall, C. J. Portier, R. C. H. Vermeulen, and S. P. Hamburg. 2017. “High-resolution air pollution mapping with Google Street View cars: Exploiting big data.” Environ. Sci. Technol. 51 (12): 6999–7008. https://doi.org/10.1021/acs.est.7b00891.
Araar, O., N. Aouf, and J. L. V. Dietz. 2015. “Power pylon detection and monocular depth estimation from inspection UAVs.” Ind. Rob. 42 (3): 200–213. https://doi.org/10.1108/IR-11-2014-0419.
Bjarnadottir, S., Y. Li, and M. G. Stewart. 2013. “Hurricane risk assessment of power distribution poles considering impacts of a changing climate.” J. Infrastruct. Syst. 19 (1): 12–24. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000108.
Boussias-Alexakis, E., V. Tsironisa, E. Petsa, and G. Karras. 2016. “Automatic adjustment of wide-base Google Street View panoramas.” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLI-B1 (Jun): 639–645. https://doi.org/10.5194/isprs-archives-XLI-B1-639-2016.
Braik, A. M., A. M. Salman, and Y. Li. 2019. “Risk-based reliability and cost analysis of utility poles subjected to tornado hazard.” J. Aerosp. Eng. 32 (4): 04019040. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001029.
Campbell, A., A. Both, and Q. C. Sun. 2019. “Detecting and mapping traffic signs from Google Street View images using deep learning and GIS.” Comput. Environ. Urban Syst. 77 (Sep): 101350. https://doi.org/10.1016/j.compenvurbsys.2019.101350.
Canny, J. 1986. “A computational approach to edge detection.” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6): 679–698. https://doi.org/10.1109/TPAMI.1986.4767851.
Cariolet, J. M., M. Vuillet, and Y. Diab. 2019. “Mapping urban resilience to disasters—A review.” Sustainable Cities Soc. 51 (Nov): 101746. https://doi.org/10.1016/j.scs.2019.101746.
Chopde, N. R., and M. K. Nichat. 2013. “Landmark based shortest path detection by using A* and Haversine formula.” Int. J. Innovative Res. Comput. Commun. Eng. 1 (2): 298–302.
CPS Energy. 2017. Pole inspection guidelines. San Antonio: CPS Energy.
Darestani, Y. M., and A. Shafieezadeh. 2019. “Multi-dimensional wind fragility functions for wood utility poles.” Eng. Struct. 183 (Mar): 937–948. https://doi.org/10.1016/j.engstruct.2019.01.048.
Deng, C., S. Wang, Z. Huang, Z. Tan, and J. Liu. 2014. “Unmanned aerial vehicles for power line inspection: A cooperative way in platforms and communications.” J. Commun. 9 (9): 687–692. https://doi.org/10.12720/jcm.9.9.687-692.
Ellingwood, B. R., D. V. Rosowsky, Y. Li, and J. H. Kim. 2004. “Fragility assessment of light-frame wood construction subjected to wind and earthquake hazards.” J. Struct. Eng. 130 (12): 1921–1930. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:12(1921).
FAA (Federal Aviation Administration). 2020a. “Restricted or special use airspace.” Accessed August 24, 2020. https://www.faa.gov/uas/recreational_fliers/where_can_i_fly/airspace_restrictions/tfr/.
FAA (Federal Aviation Administration). 2020b. “ENR 5.1 prohibited, restricted, and other areas.” Accessed August 24, 2020. https://www.faa.gov/air_traffic/publications/atpubs/aip_html/part2_enr_section_5.1.html.
Gay-Bellile, V., A. Bartoli, and P. Sayd. 2007. “Direct estimation of non-rigid registrations with image-based self-occlusion reasoning.” In Proc., IEEE Int. Conf. on Computer Vision. New York: IEEE.
Goto, Y., S. Matsuda, M. Waki, and H. Oshida. 2020. “Structural deterioration determination system for improving the efficiency of utility pole inspection.” In Proc., 2020 21st Asia-Pacific Network Operations and Management Symp. (APNOMS), 61–66. New York: IEEE.
Gulf Power. 2018. “Unprecedented response from storm team of more than 7,500—Oct. 20, 2018 | Gulf Power News.” Accessed December 31, 2019. https://www.gulfpowernews.com/unprecedented-response-from-storm-team-of-more-than-7500/.
Ham, Y., and M. Kamari. 2019. “Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones.” Autom. Constr. 105 (Sep): 102831. https://doi.org/10.1016/j.autcon.2019.102831.
Ham, Y., and J. Kim. 2020. “Participatory sensing and digital twin city: Updating virtual city models for enhanced risk-informed decision-making.” J. Manage. Eng. 36 (3): 04020005. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000748.
Hara, K., V. Le, and J. E. Froehlich. 2013a. “Combining crowdsourcing and Google Street View to identify street-level accessibility problems.” Proc., SIGCHI Conf. on Human Factors in Computing Systems, 631–640. New York: ACM Press.
Hara, K., V. Le, J. Sun, D. Jacobs, and J. E. Froehlich. 2013b. Exploring early solutions for automatically identifying inaccessible sidewalks in the physical world using Google Street View. Pacific Grove, CA: Human Computer Interaction Consortium.
He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep residual learning for image recognition.” In Proc., IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 770–778. New York: IEEE.
Hebbalaguppe, R., G. Garg, E. Hassan, H. Ghosh, and A. Verma. 2017. “Telecom inventory management via object recognition & localisation on google street view images.” In Proc., 2017 IEEE Winter Conf. on Applications of Computer Vision (WACV 2017), 725–733. New York: IEEE.
Hosseini, M. M., A. Umunnakwe, M. Parvania, and T. Tasdizen. 2020. “Intelligent damage classification and estimation in power distribution poles using unmanned aerial vehicles and convolutional neural networks.” IEEE Trans. Smart Grid 11 (4): 3325–3333. https://doi.org/10.1109/TSG.2020.2970156.
IEEE. 2016. National electrical safety code. 2017 ed. New York: IEEE.
Kamari, M., and Y. Ham. 2018. “Automated filtering big visual data from drones for enhanced visual analytics in construction.” In Proc., Construction Research Congress 2018: Construction Information Technology, 398–409. Reston, VA: ASCE.
Kamari, M., and Y. Ham. 2020. “Analyzing potential risk of wind-induced damage in construction sites and neighboring communities using large-scale visual data from drones.” Construction Research Congress 2020: Computer Applications. Reston, VA: ASCE.
Katra, J., and F. Pernu. 2010. “A survey of mobile robots for distribution power line inspection.” IEEE Trans. Power Delivery 25 (1): 485–493. https://doi.org/10.1109/TPWRD.2009.2035427.
Keshavarzian, M., and C. H. Priebe. 2002. “Wind performance of short utility pole structures.” Pract. Period. Struct. Des. Constr. 7 (4): 141–146. https://doi.org/10.1061/(ASCE)1084-0680(2002)7:4(141).
Kim, H., and Y. Ham. 2021. “Increasing reliability of participatory sensing for utility pole condition assessment using fuzzy inference.” J. Constr. Eng. Manage. 147 (1): 04020154. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001968.
Kim, J., and Y. Ham. 2020. “Vision-based analysis of utility poles using drones and digital twin modeling in the context of power distribution infrastructure systems.” In Proc., Construction Research Congress 2020: Construction Information Technology. Reston, VA: ASCE.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. “ImageNet classification with deep convolutional neural networks.” Commun. ACM 60 (6): 84–90. https://doi.org/10.1145/3065386.
Lee, S., and Y. Ham. 2020. “Probabilistic analysis of structural reliability of leaning poles in power distribution infrastructure systems under extreme wind loads.” In Proc., Construction Research Congress 2020: Construction Information Technology. Reston, VA: ASCE.
Lee, S., and Y. Ham. 2021. “Probabilistic framework for assessing the vulnerability of power distribution infrastructures under extreme wind conditions.” Sustainable Cities Soc. 65 (Feb): 102587. https://doi.org/10.1016/j.scs.2020.102587.
Matikainen, L., M. Lehtomäki, E. Ahokas, J. Hyyppä, M. Karjalainen, A. Jaakkola, A. Kukko, and T. Heinonen. 2016. “Remote sensing methods for power line corridor surveys.” ISPRS J. Photogramm. Remote Sens. 119 (Sep): 10–31. https://doi.org/10.1016/j.isprsjprs.2016.04.011.
Matisziw, T. C., and A. T. Murray. 2009. “Modeling s-t path availability to support disaster vulnerability assessment of network infrastructure.” Comput. Oper. Res. 36 (1): 16–26. https://doi.org/10.1016/j.cor.2007.09.004.
Mohammadi Darestani, Y., A. Shafieezadeh, and R. DesRoches. 2016. “An equivalent boundary model for effects of adjacent spans on wind reliability of wood utility poles in overhead distribution lines.” Eng. Struct. 128 (Dec): 441–452. https://doi.org/10.1016/j.engstruct.2016.09.052.
Montero, D., and C. Rueda. 2018. “Detection of palm oil bud rot employing artificial vision.” IOP Conf. Ser.: Mater. Sci. Eng. 437 (1): 012004. https://doi.org/10.1088/1757-899X/437/1/012004.
NERC (North American Electric Reliability Corporation). 2018. Hurricane harvey event analysis report. Atlanta: NERC.
OpenStreetMap. 2020. “OpenStreetMap.” Accessed September 1, 2020. https://www.openstreetmap.org/history#map=5/37.996/-95.845.
OpenWeather. 2020. “Current weather and forecast—OpenWeatherMap.” Accessed September 1, 2020. https://openweathermap.org/.
Overpass turbo. 2020. “Overpass Turbo.” Accessed September 1, 2020. https://overpass-turbo.eu/.
Qayyum, A., A. S. Malik, N. M. Saad, M. F. bin Abdullah, M. Iqbal, W. Rasheed, A. R. Bin Ab Abdullah, and M. Y. Hj Jaafar. 2017. “Measuring height of high-voltage transmission poles using unmanned aerial vehicle (UAV) imagery.” Imaging Sci. J. 65 (3): 137–150. https://doi.org/10.1080/13682199.2017.1299304.
Ryan, P. C., M. G. Stewart, N. Spencer, and Y. Li. 2014. “Reliability assessment of power pole infrastructure incorporating deterioration and network maintenance.” Reliab. Eng. Syst. Saf. 132 (Dec): 261–273. https://doi.org/10.1016/j.ress.2014.07.019.
Ryu, H., J. S. Lee, and S. Lee. 2018. “Participatory neighborhood revitalization effects on social capital: Evidence from community building projects in Seoul.” J. Urban Plann. Dev. 144 (1): 04017025. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000416.
Salman, A. M., Y. Li, and E. Bastidas-Arteaga. 2017. “Maintenance optimization for power distribution systems subjected to hurricane hazard, timber decay and climate change.” Reliab. Eng. Syst. Saf. 168 (Dec): 136–149. https://doi.org/10.1016/j.ress.2017.03.002.
Sharma, H., V. Adithya, T. Dutta, and P. Balamuralidhar. 2015. “Image analysis-based automatic utility pole detection for remote surveillance.” In Proc., 2015 Int. Conf. on Digital Image Computing: Techniques and Applications (DICTA 2015). New York: IEEE.
Teoh, Y. E., A. Alipour, and A. Cancelli. 2019. “Probabilistic performance assessment of power distribution infrastructure under wind events.” Eng. Struct. 197 (Oct): 109199. https://doi.org/10.1016/j.engstruct.2019.05.041.
Varghese, A., J. Gubbi, H. Sharma, and P. Balamuralidhar. 2017. “Power infrastructure monitoring and damage detection using drone captured images.” In Proc., of the Int. Joint Conf. on Neural Networks, 1681–1687. New York: IEEE.
Wang, C.-H., R. H. Leicester, and M. Nguyen. 2008. “Probabilistic procedure for design of untreated timber poles in-ground under attack of decay fungi.” Reliab. Eng. Syst. Saf. 93 (3): 476–481. https://doi.org/10.1016/j.ress.2006.12.007.
Xiong, W., C. S. Cai, B. Kong, and J. Ye. 2017. “Overturning-collapse modeling and safety assessment for bridges supported by single-column piers.” J. Bridge Eng. 22 (11): 04017084. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001133.
Yuan, H., W. Zhang, J. Zhu, and A. C. Bagtzoglou. 2018. “Resilience assessment of overhead power distribution systems under strong winds for hardening prioritization.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 4 (4): 04018037. https://doi.org/10.1061/AJRUA6.0000988.
Zhang, W., C. Witharana, W. Li, C. Zhang, X. Li, and J. Parent. 2018. “Using deep learning to identify utility poles with crossarms and estimate their locations from Google Street View images.” Sensors 18 (8): 2484. https://doi.org/10.3390/s18082484.
Zhang, Y., X. Yuan, Y. Fang, and S. Chen. 2017. “UAV low altitude photogrammetry for power line inspection.” ISPRS Int. J. Geo-Inf. 6 (1): 14. https://doi.org/10.3390/ijgi6010014.
Zhu, Z., J. Zhang, M. Alam, B. Eren Tokgoz, and S. Hwang. 2019. “Automatic utility pole inclination angle measurement using unmanned aerial vehicle and deep learning.” In Proc., of the Institute of Industrial and Systems Engineers Annual Conf. 2019. Alexandria, VA: National Science Foundation.
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Journal of Construction Engineering and Management
Volume 147 • Issue 10 • October 2021
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© 2021 American Society of Civil Engineers.
History
Received: Sep 21, 2020
Accepted: May 21, 2021
Published online: Jul 30, 2021
Published in print: Oct 1, 2021
Discussion open until: Dec 30, 2021
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