Resource Assessment Tool for Effective Unmanned-Aerial-Vehicle-Assisted Bridge Inspections

被引:0
|
作者
Marfo, Emmanuel A. [1 ]
Khan, Mubbashar A. [2 ]
Wu, Tau [3 ]
Cavalline, Tara L. [3 ]
Karimoddini, Ali [1 ]
机构
[1] North Carolina Agr & Tech State Univ, Elect & Comp Engn Dept, Greensboro, NC 27411 USA
[2] Cent State Univ, Mfg Engn Dept, Wilberforce, OH USA
[3] Univ North Carolina Charlotte, Dept Engn Technol & Construct Management, Charlotte, NC USA
关键词
aircraft/airport compatibility; aviation; bridge and structures management; bridge condition data/assessment; infrastructure; infrastructure management and system preservation; operations;
D O I
10.1177/03611981241260701
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
It is critically important to plan properly for integrating and deploying unmanned aerial vehicles (UAVs) in the bridge inspection process, there is a need for tools to support implementation and decision-making regarding the use of UAVs at specific structures. In this study, a resource estimation tool that can be used to estimate the resources required for UAV-assisted bridge inspections is developed. The tool can aid inspectors in determining the estimated flight time and resources required for using a specific UAV and operator during the inspection of a specific bridge. The tool supports the development of optimal flight paths based on the structural geometry and positioning of structural elements of a bridge, establishes a range of recommended flight speeds for conducting reliable UAV-assisted bridge inspections based on the skill level(s) of the pilot(s) who were involved in conducting inspections. The developed tool also establishes a recommended range of wind speed and the corresponding standoff clearance information for safely conducting UAV-assisted bridge inspections. The tool also provides an estimated number of batteries required to allow the estimated required flight time. In this paper, the development of the tool is described, and the advantages of the tool are illustrated by its application in a case study involving a 10-span steel continuous multi-beam bridge with a reinforced concrete deck. The tool is developed as a spreadsheet and is publicly available through a GitHub page, accessible at https://github.com/ACCESSLab/Resource-Assessment-Tool-for-Effective-UAV-Assisted-Bridge-Inspection.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Joint Sensing and Communications in Unmanned-Aerial-Vehicle-Assisted Systems
    Bithas, Petros S.
    Efthymoglou, George P.
    Kanatas, Athanasios G.
    Maliatsos, Konstantinos
    DRONES, 2024, 8 (11)
  • [2] Unmanned-Aerial-Vehicle-Assisted Wireless Networks: Advancements, Challenges, and Solutions
    Dai, Minghui
    Huang, Ning
    Wu, Yuan
    Gao, Jie
    Su, Zhou
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (05) : 4117 - 4147
  • [3] Stochastic Coded Offloading Scheme for Unmanned-Aerial-Vehicle-Assisted Edge Computing
    Ng, Wei Chong
    Lim, Wei Yang Bryan
    Xiong, Zehui
    Niyato, Dusit
    Miao, Chunyan
    Han, Zhu
    Kim, Dong In
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (07) : 5626 - 5643
  • [4] Utilization of Unmanned Aerial Vehicle, Artificial Intelligence, and Remote Measurement Technology for Bridge Inspections
    Chun, Pang-jo
    Dang, Ji
    Hamasaki, Shunsuke
    Yajima, Ryosuke
    Kameda, Toshihiro
    Wada, Hideki
    Yamane, Tatsuro
    Izumi, Shota
    Nagatani, Keiji
    JOURNAL OF ROBOTICS AND MECHATRONICS, 2020, 32 (06) : 1244 - 1258
  • [5] Unmanned Aerial Vehicle Assisted Healthcare Resource Allocation in Disasters
    Diao, Li
    Liu, Yue
    Liu, William
    Chiaraviglio, Luca
    2022 32ND INTERNATIONAL TELECOMMUNICATION NETWORKS AND APPLICATIONS CONFERENCE (ITNAC), 2022, : 137 - 141
  • [6] Unmanned-Aerial-Vehicle-Assisted Computation Offloading for Mobile Edge Computing Based on Deep Reinforcement Learning
    Wang, Hui
    Ke, Hongchang
    Sun, Weijia
    IEEE ACCESS, 2020, 8 : 180784 - 180798
  • [7] Joint optimization task offloading and trajectory control for unmanned-aerial-vehicle-assisted mobile edge computing
    Xu, Fei
    Wang, Sen
    Su, Weiya
    Zhang, Lin
    COMPUTERS & ELECTRICAL ENGINEERING, 2023, 111
  • [8] Joint Stochastic Computation Offloading and Trajectory Optimization for Unmanned-Aerial-Vehicle-Assisted Mobile Edge Computing
    Zhou, Yi
    IEEE ACCESS, 2025, 13 : 2034 - 2044
  • [9] Unmanned-aerial-vehicle-assisted cooperative communications for visible light communications-based vehicular networks
    Kalikulov, Nurzhan
    Kizilirmak, Refik Cagier
    Uysal, Murat
    OPTICAL ENGINEERING, 2019, 58 (08)
  • [10] Autonomous Control System with Passive Positioning for Unmanned-Aerial-Vehicle-Assisted Edge Communication in 6G
    Hu, Yue
    Jiang, Yunzhe
    Liu, Yinqiu
    He, Xiaoming
    APPLIED SCIENCES-BASEL, 2023, 13 (19):