Publication Cover
Structure and Infrastructure Engineering
Maintenance, Management, Life-Cycle Design and Performance
Volume 18, 2022 - Issue 3
7,279
Views
12
CrossRef citations to date
0
Altmetric
Articles

Hazards identification and risk assessment for UAV–assisted bridge inspections

, &
Pages 412-428 | Received 14 Apr 2020, Accepted 26 Oct 2020, Published online: 07 Jan 2021

References

  • Aven, T., Renn, O., & Rosa, E. A. (2011). On the ontological status of the concept of risk. Safety Science, 49(8-9), 1074–1079. doi:https://doi.org/10.1016/j.ssci.2011.04.015
  • Ayele, Y. Z. (2019). Drones for inspecting aging bridges. Paper presented at the International Conference on Natural Hazards and Infrastructure. ISSN 2623-4513., Chania, Crete Island, Greece.
  • Ayele, Y. Z., Barabadi, A., & Barabady, J. (2016). Dynamic spare parts transportation model for Arctic production facility. International Journal of System Assurance Engineering and Management, 7(1), 84–98.
  • Ayele, Y. Z., & Droguett, E. L. (2019). Application of UAVs for bridge inspection and resilience assessment. Paper presented at the 29th European Safety and Reliability Conference, Hannover, Germany.
  • Barabadi, A., & Markeset, T. (2011). Reliability and maintainability performance under Arctic conditions. International Journal of System Assurance Engineering and Management, 2(3), 205–217.
  • Belcastro, C. M., Newman, R. L., Evans, J., Klyde, D. H., Barr, L. C., & Ancel, E. (2017). Hazards identification and analysis for unmanned aircraft system operations. Paper presented at the 17th AIAA Aviation Technology, Integration, and Operations Conference. doi:https://doi.org/10.2514/6.2017-3269
  • Benamara, F., Kaci, S., & Pigozzi, G. (2010). Individual opinions-based judgment aggregation procedures. Paper presented at the International Conference on Modeling Decisions for Artificial Intelligence.
  • Burdett, H., Stoker, J., & Simpson, A. (2009). Functional Hazard Assessment (FHA) report for unmanned aircraft systems. Eurocontrol, 1, 1–81.
  • Cai, H., & Lin, Y. (2011). Modeling of operators' emotion and task performance in a virtual driving environment. International Journal of Human-Computer Studies, 69(9), 571–586. doi:https://doi.org/10.1016/j.ijhcs.2011.05.003
  • Cha, Y.-J., Choi, W., & Büyüköztürk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. doi:https://doi.org/10.1111/mice.12263
  • Cha, Y.-J., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (2018). Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 33(9), 731–747. doi:https://doi.org/10.1111/mice.12334
  • Ciampa, E., De Vito, L., & Pecce, M. R. (2019). Practical issues on the use of drones for construction inspections. Paper presented at the Journal of Physics: Conference Series. doi:https://doi.org/10.1088/1742-6596/1249/1/012016
  • Clothier, R. A., & Walker, R. A. (2015). The safety risk management of unmanned aircraft systems. In: Valavanis K., Vachtsevanos G. (eds). Springer, Dordrecht. doi:https://doi.org/10.1007/978-90-481-9707-1_39
  • DeYoung, R. (2018, December). Aviator pro, drones, and falling into the vortex ring state. Retrieved from https://www.agi.com/articles/aviator-pro-drones-and-falling-into-the-vortex-rin.
  • Foreman, V. L., Favaró, F. M., Saleh, J. H., & Johnson, C. W. (2015). Software in military aviation and drone mishaps: Analysis and recommendations for the investigation process. Reliability Engineering & System Safety, 137, 101–111.
  • Gielo-Perczak, K., & Karwowski, W. (2003). Ecological models of human performance based on affordance, emotion and intuition. Ergonomics, 46(1-3), 310–326. doi:https://doi.org/10.1080/00140130303536
  • Gudmestad, O., Alhimenko, A., Løset, S., Shkhinek, K., Tørum, A., & Jensen, A. (2007). Engineering aspects related to Arctic offshore developments. St. Petersburg, Lan, 1–255.
  • Hayhurst, K. J., Maddalon, J. M., Miner, P. S., DeWalt, M. P., & McCormick, G. F. (2006). Unmanned aircraft hazards and their implications for regulation. Paper presented at the 2006 ieee/aiaa 25TH Digital Avionics Systems Conference. doi:https://doi.org/10.1109/DASC.2006.313735
  • Hora, S. C. (2009). Expert Judgement in Risk Analysis. Retrieved from http://create.usc.edu/sites/default/files/publications/expertjudgmentinriskanalysis_0.pdf.
  • Southam, J. (2018). GPS common problems and how to mitigate them. Retrieved from https://safety4sea.com/gps-common-problems-and-how-to-mitigate-them/.
  • Jung, H.-J., Lee, J.-H., & Kim, I.-H. (2018). Challenging issues and solutions of bridge inspection technology using unmanned aerial vehicles. Paper presented at the Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018. doi:https://doi.org/10.1117/12.2300957
  • Kang, D., & Cha, Y.-J. (2018). Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging. Computer-Aided Civil and Infrastructure Engineering, 33(10), 885–902. doi:https://doi.org/10.1111/mice.12375
  • Kumar, R., Barabady, J., Markeset, T., & Kumar, U. (2009). Improvement of performance of oil and gas production facilities in Arctic regions by applying human factors/ergonomic principles at the design phase. Paper presented at the. Proceedings of the 20th International Conference on Port and Ocean Engineering under Arctic Conditions: June 9-12, 2009, Luleå, Sweden.
  • Liu, M., Frangopol, D. M., & Kim, S. (2009). Bridge system performance assessment from structural health monitoring: A case study. Journal of Structural Engineering, 135(6), 733–742. doi:https://doi.org/10.1061/(ASCE)ST.1943-541X.0000014
  • Lootsma, F. A. (1991). Scale sensitivity and rank preservation in a multiplicative variant of the AHP and SMART.
  • Lu, C., Lan, J., & Wang, Z. (2006). Aggregation of fuzzy opinions under group decision-making based on similarity and distance. Journal of Systems Science and Complexity, 19(1), 63–71. doi:https://doi.org/10.1007/s11424-006-0063-y
  • Maldonado, E., Casas, J., & Canas, J. A. (2019). Modelos de vulnerabilidad sísmica de puentes basado en" conjuntos difusos. Monograph Series in Earthquake Engineering, Editor A.H. Barbat, 1, 1–63.
  • Markeset, T. (2008). Design for high performance assurance for offshore production facilities in remote harsh and sensitive environments. OPSEARCH, 45(3), 275–290. doi:https://doi.org/10.1007/BF03398819
  • Moud, H. I., Shojaei, A., Flood, I., Zhang, X., Hatami, M., & Rinker, M. (2018). Qualitative and quantitative risk analysis of unmanned aerial vehicle flights over construction job sites. Paper presented at the Proceedings of the Eighth International Conference on Advanced Communications and Computation (INFOCOMP 2018), Barcelona, Spain.
  • NCHRP. (2017). New guidelines for NDE bridge inspection methods. Retrieved from https://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_readyresult_08.pdf.
  • Ortiz, N., Wheeler, T., Breeding, R., Hora, S., Meyer, M., & Keeney, R. (1991). Use of expert judgment in NUREG-1150. Nuclear Engineering and Design, 126(3), 313–331. doi:https://doi.org/10.1016/0029-5493(91)90023-B
  • Phares, B. M., Rolander, D. D., Graybeal, B. A., & Washer, G. A. (2001). Reliability of visual bridge inspection. Public Roads, 64(5), 64.
  • Rakha, T., & Gorodetsky, A. (2018). Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones. Automation in Construction, 93, 252–264. doi:https://doi.org/10.1016/j.autcon.2018.05.002
  • Ramanathan, R., & Ganesh, L. (1994). Group preference aggregation methods employed in AHP: An evaluation and an intrinsic process for deriving members' weightages. European Journal of Operational Research, 79(2), 249–265. doi:https://doi.org/10.1016/0377-2217(94)90356-5
  • Seddon, J. M., & Newman, S. (2011). Basic helicopter aerodynamics (Vol. 40). New Jersey, USA: John Wiley & Sons.
  • Seo, J., Wacker, J. P., & Duque, L. (2018). Evaluating the use of drones for timber bridge inspection. Gen. Tech. Rep. FPL-GTR-258. Madison, WI: US Department of Agriculture, Forest Service, Forest Products Laboratory, 1-152(258)1152.
  • Tummala, V. M. R., & Ling, H. (1996). Sampling distribution of the random consistency index of the Analytic Hierarchy Process (AHP). Journal of Statistical Computation and Simulation, 55(1-2), 121–131. doi:https://doi.org/10.1080/00949659608811754
  • Tummala, V. M. R., & Ling, H. (1998). A note on the computation of the mean random consistency index of the analytic hierarchy process (AHP). Theory and Decision, 44(3), 221–230. Retrieved from < Go to ISI>://WOS:000075460100002
  • Tummala, V. M. R., & Wan, Y. (1994). On the mean random inconsistency index of analytic hierarchy process (Ahp). Computers & Industrial Engineering, 27(1-4), 401–404.
  • Tung, S. L., & Tang, S. L. (1998). A comparison of the Saaty's AHP and modified AHP for right and left eigenvector inconsistency. European Journal of Operational Research, 106(1), 123–128. doi:https://doi.org/10.1016/S0377-2217(98)00353-1
  • Wackwitz, K., & Boedecker, H. (2015). Safety risk assessment for uav operation. Drone Industry Insights, Safe Airspace Integration Project, Part One, Hamburg, Germany.