Benchmarking Unmanned Aerial Systems-Assisted Inspection of Steel Bridges for Fatigue Cracks

Inspection agencies have been increasingly implementing unmanned aerial systems (UAS) for bridge inspections. Currently, UAS are typically used for long-range monitoring and surveillance tasks, but bridge managers are hopeful that they may be utilized for detailed inspection, such as condition assessments and the inspection of fracture critical members (FCMs) in the near future. As an assistive tool for visual inspections, the accuracy of UAS-assisted inspections is unknown. This study investigates the relationship between the characteristics of the individual inspectors and a set of performance metrics associated with UAS-assisted FCM inspections. Four bridge inspectors used a UAS to inspect a series of full-sized bridge specimens with known fatigue cracks. The inspection videos were later shared with 19 bridge inspectors for a desk review. The performance of each inspector was evaluated and compared with the results from 30 hands-on inspections of the same specimens. The results showed that an inspector’s past experience with UAS, licensure, and academic degree could have a significant influence on one or more of the three defined performance metrics. The comparison between the results of the UAS-assisted inspections and the hands-on inspections revealed that crack detection was comparable. However, the hands-on inspections were more accurate.

[1]  Arthur Gretton,et al.  Consistent Nonparametric Tests of Independence , 2010, J. Mach. Learn. Res..

[2]  Zain Anwar Ali,et al.  An overview of various kinds of wind effects on unmanned aerial vehicle , 2019, Measurement and Control.

[3]  Abba G. Lichtenstein,et al.  The Silver Bridge Collapse Recounted , 1993 .

[4]  Robert J. Thomas,et al.  Fatigue Crack Detection Using Unmanned Aerial Systems in Fracture Critical Inspection of Steel Bridges , 2018, Journal of Bridge Engineering.

[5]  F. Ferris,et al.  New visual acuity charts for clinical research. , 1982, American journal of ophthalmology.

[6]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[7]  James Joseph Biundo,et al.  Analysis of Contingency Tables , 1969 .

[8]  Calvin Coopmans,et al.  Fatigue Crack Detection Using Unmanned Aerial Systems in Under-Bridge Inspection , 2017 .

[9]  Guido Morgenthal,et al.  Quality Assessment of Unmanned Aerial Vehicle (UAV) Based Visual Inspection of Structures , 2014 .

[10]  George Hearn,et al.  Bridge Inspection Practices , 2007 .

[11]  Calvin Coopmans,et al.  A Practitioner’s Guide to Small Unmanned Aerial Systems for Bridge Inspection , 2019, Infrastructures.

[12]  Robert J. Connor,et al.  Benchmark for Evaluating Performance in Visual Inspection of Fatigue Cracking in Steel Bridges , 2020 .

[13]  Walter W. Piegorsch,et al.  TABLES OF P-VALUES FOR t- AND CHI-SQUARE REFERENCE DISTRIBUTIONS , 1997 .

[14]  Marc Maguire,et al.  Bridge inspection: human performance, unmanned aerial systems and automation , 2018, Journal of Civil Structural Health Monitoring.

[15]  Glenn Washer,et al.  Reliability of visual inspection for highway bridges , 2001 .

[16]  Robert J. Connor,et al.  Inspection and Management of Bridges with Fracture-Critical Details , 2005 .

[17]  Denis G. Pelli,et al.  THE DESIGN OF A NEW LETTER CHART FOR MEASURING CONTRAST SENSITIVITY , 1988 .

[18]  Robert J. Connor,et al.  Proposed Method for Determining the Interval for Hands-on Inspection of Steel Bridges with Fracture Critical Members , 2010 .

[19]  Sandra G. Hart,et al.  Nasa-Task Load Index (NASA-TLX); 20 Years Later , 2006 .

[20]  Robert J. Thomas,et al.  Deep Learning Neural Networks for sUAS-Assisted Structural Inspections: Feasibility and Application , 2018, 2018 International Conference on Unmanned Aircraft Systems (ICUAS).

[21]  Robert J. Connor,et al.  Quality of Element-Level Bridge Inspection Data , 2020 .

[22]  Andrzej S. Nowak,et al.  Reliability of Structures , 2000 .

[23]  Duzgun Agdas,et al.  Comparison of visual inspection and structural-health monitoring as bridge condition assessment methods , 2016 .