Fatigue Crack Detection Using Unmanned Aerial Systems in Fracture Critical Inspection of Steel Bridges-Reference-Cited by-同舟云学术

Fatigue Crack Detection Using Unmanned Aerial Systems in Fracture Critical Inspection of Steel Bridges

Published:2018-10 Issue:10 Volume:23 Page:04018078
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Dorafshan Sattar¹,Thomas Robert J.²,Maguire Marc³

Affiliation:

1. Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Utah State Univ., 4110 Old Main Hill, Logan, UT 84321 (corresponding author).

2. Postdoctoral Research Associate, Dept. of Civil and Environmental Engineering, Utah State Univ., 4110 Old Main Hill, Logan, UT 84321.

3. Assistant Professor, Dept. of Civil and Environmental Engineering, Utah State Univ., 4110 Old Main Hill, Logan, UT 84321.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Building and Construction,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29BE.1943-5592.0001291

Reference28 articles.

1. Adams S. M. and C. J. Friedland. 2011. “A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management.” In Proc. 9th Int. Workshop on Remote Sensing for Disaster Response 8. California: MCEER Bulletin Stanford University.

2. Collapse of Steel Bridges

3. Brooks C. R. J. Dobson D. M. Banach D. Dean T. Oommen R. E. Wolf and B. Hart. 2015. “Evaluating the use of unmanned aerial vehicles for transportation purposes (No. RC-1616).” Ann Arbor Michigan: Michigan Tech Research Institute. Accessed November 7 2018. https://www.michigan.gov/documents/mdot/RC1616_Part_A_488515_7.pdf.

4. Dorafshan S. C. Coopmans R. Thomas and M. Maguire. 2018. “Deep learning neural networks for sUAS-assisted structural inspections: Feasibility and application.” In Proc. Int. Conf. on Unmanned Aircraft Systems (ICUAS). New York: IEEE.

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