UAV Selection Methodology and
Performance Evaluation to Support
UAV-Enabled Bridge Inspection

Ali Karimoddini, Ph.D.
Tara L. Cavalline, Ph.D., P.E.
Beth Smith, Ph.D., P.E.
Rodward Hewlin, Ph.D.
Abdollah Homaifar, Ph.D.
Autonomous Cooperative Control of Emergent Systems of Systems (ACCESS) Laboratory
Electrical and Computer Engineering Department
North Carolina A&T State University
1601 East Market Street
Greensboro, NC, 27411
And
The University of NC at Charlotte
9201 University City Blvd.
Charlotte, NC 28223-0001

Report Number: NCDOT2020-23
NCDOT Project RP2020-23
March 2022

1

Technical Report Documentation Page
1.

Report No. FHWA/NC/2020-23

2.

Government Accession No.

4. Title and Subtitle
UAV Selection Methodology and Performance Evaluation to Support UAV-Enabled
Bridge Inspection

3.

Recipient’s Catalog No.

5.

Report Date March 15, 2022

6.

Performing Organization Code
Performing Organization Report No.

7.

Author(s)Ali Karimoddini, Tara L. Cavalline, Beth Smith, Rodward Hewlin, Abdullah
Homaifar8.

9.

Performing Organization Name and Address
North Carolina A&T State University, Electrical and Computer Engineering
Department, 1601 East Market Street, Greensboro, NC, 27411, And
University of North Carolina at Charlotte, Department of Engineering Technology
and Construction Management, 9201 University City Blvd., Charlotte, NC 282230001

10. Work Unit No. (TRAIS)

11. Contract or Grant No.
12. Sponsoring Agency Name and Address North
Carolina Department of Transportation
Research and Development Unit

13. Type of Report and Period Covered
Final ReportAugust 1, 2019 – October 31, 2021

104 Fayetteville Street
Raleigh, North Carolina 27601

14. Sponsoring Agency CodeRP 2020-23
Supplementary Notes:
16. Abstract

This project performed preliminary work to support use of Unmanned Aerial Vehicles (UAV)-based for bridge inspections,providing an economical and safer alternative to conventional inspection practices. The main challenge is that most existing
technologies rely on general-purpose UAV platforms and there is no verified methodology for UAV-enabled bridge inspection
principles and relevant considerations to reliably obtain inspection data. There have been some efforts to use general-purpose
commercially available UAVs for bridge inspection. However, the turbulent environment that often exists around bridges requires
customized and enhanced UAV platforms with a higher level of robustness, taking into account the bridge type and structure aswell as the weather conditions around the bridge. Additionally, the data-acquisition capabilities of commercially available UAVs
need to be compared to those required for bridge inspection. Previously, there has not been a study to quantify the gap between theperformance of the commercially available UAVs and ideal desired performances. In this multidisciplinary project, acomprehensive set of experiments were developed for selection, testing, and evaluation techniques of candidate UAVs, thecomplex nature of flying UAVs in close proximity to bridges was explored, and the limitations of UAV flight due to turbulent
flows around bridge components and nearby terrain was assessed. Commercially available platforms for bridge inspection wereselected, tested, and evaluated. Deliverables from this project include: (1) measurable metrics to evaluate the performance of
UAVs for bridge inspection, (2) experiments to test the suitability of UAVs for bridge inspection, and (3) a comprehensive
analysis near-bridge environment flow field. Computational analysis of air flow patterns near bridge elements shows that thebridge geometry creates areas of turbulence and flow variation which impact the control requirements of the UAV. Local weather
conditions can amplify these areas. Test flights were performed at selected structures to provide additional insight into the flightand data collection capabilities of the UAVs under consideration. Findings and deliverables from this project will help NCDOT
justify capital purchases made to support UAV-assisted inspection, as well as additional research needed to integrate UAVs into
their current bridge inspection processes. Ultimately, this work supports a follow-up project to develop workflows andimplementation tools for efficient UAV-enabled bridge inspection.
17.
19. Security Classif. (of this report)
Unclassified
Form DOT F 1700.7 (8-72)

18. Distribution Statement
20. Security Classif. (of this page)
Unclassified
Reproduction of completed page authorized.

2

21. No. of Pages
65

22. Price

DISCLAIMER
The contents of this report reflect the views of the author(s) and not necessarily the views of the University.
The author(s) are responsible for the facts and the accuracy of the data presented herein. The contents do
not necessarily reflect the official views or policies of either the North Carolina Department ofTransportation or the Federal Highway Administration at the time of publication. This report does notconstitute a standard, specification, or regulation.

3

ACKNOWLEDGMENTS
This research project was sponsored by the North Carolina Department of Transportation (NCDOT) andtheir continued support is greatly appreciated. The research team would like to express their appreciation
to the following:●

The partners from industry, namely, Digital Aerolus, Skydio, Parrot ANAFI, and Intel for
providing their Unmanned Aerial Vehicles and the technical support throughout the project.

●

The NCDOT personnel serving on the Steering and Implementation Committee for this researchstudy. In particular, we would like to thank the feedback received from the Steering and
Implementation Committee Chair Thomas Walls, Co-Chair Gichuru Muchane, and the members(Nick Pierce, David Snoke, Basil Yap, Wendee Smith, Aaron Earwood, Daniel Muller, Reuben
Blakely, Brian Hedrick, and Matthew Walker) during the course of this project is greatly
appreciated.

●

NCDOT Research and Development personnel, particularly Mustan Kadhibhai.

●

Dr. Navid Goudarzi, the former Co-PI of the project.

●

The former Chair of the Steering and Implementation Committee, Mr. Darshan Divakaran.

●

Mr. Bruce Scuffham of RS&H, Inc., Mr. Luis Molina of Dragados USA, and Mr. Chris Stewartof NCDOT for their assistance with mounting and installation of the weather station.

4

EXECUTIVE SUMMARY
The growing demand for the regular inspection of bridges requires more efficient and cost-effective
alternative solutions. This project advanced the capabilities of NCDOT to use Unmanned Aerial Vehicles(UAV)-based bridge inspections to provide an economical and safer alternative to conventional inspection
practices. The main challenge is that most existing technologies rely on general-purpose UAV platforms
and there is no verified methodology for UAV-enabled bridge inspection principles and relevant
considerations to reliably obtain inspection data. Hence, the goal of this multidisciplinary project was toaddress the complex nature of flying UAVs in close proximity of bridges by (1) providing a systematic
procedure for selecting UAVs and (2) testing and evaluating the performance of commercially available
platforms for bridge inspection in both computational fluid dynamic model simulations and field trials.
Deliverables from this project include: (1) measurable metrics to evaluate the performance of UAVs
for bridge inspection, (2) experiments to test and evaluate the performance of the UAVs for bridge
inspection, and (3) a comprehensive analysis of near-bridge environment flow fields.
Findings from this project revealed that there is great promise to use of UAVs to assist bridge inspectors
working for NCDOT. The measurable metrics used to evaluate candidate UAVs for inspection identified
during this work should allow NCDOT personnel to evaluate commercially available UAVs for purchaseand use, as they aim to increase their inventory of UAVs to support this effort. The developed experiments
can be used as a procedure for the certification of commercially available UAVs bridge applications.
During this project UAVs from Digital Aerolus, Skydo, Intel, and Parrot participated in the experiments,
and provided technical supports and insights during the experiments. As new technologies and othercommercially available UAVs come to market, the capabilities and utility of other platforms can beassessed using the tests developed as part of this work. Our intention in this project was not to select aparticular UAV, rather was to develop a procedure that can be used to select UAVs based on the priorities
and desired parameters. Therefore, to avoid commercial advertisement and biasing toward any of theparticipant UAVs, we do not compare the performance of the UAVs with each other, and instead, we referto the participant UAVs as UAV 1, UAV 2, UAV 3, and UAV4.
Computational fluid dynamics analysis of a selected bridge and the participant UAVs revealed that thecommercially available UAVs should be capable of flying safely around most structural components.
Solid elements such as the gusset plate in each diaphragm and the bents create regions of low velocity air.
The UAV controls appear successfully transition from higher velocity regions through the open parts ofthe bridge geometry and the lower velocity, shielded regions. Maximum velocities and turbulence values
near bridge elements are amplified by wind conditions. At this time specific weather guidelines have notbeen developed.
Flight tests performed at a ten-span structure using two UAVs provided insight into the feasibility andlogistics associated with UAV-assisted inspection. Information regarding setup, workflow, and datacollection was obtained, along with preliminary images that were compared to those presented in the mostrecent inspection report for the structure. The resolution of the images provided a level of detailcomparable to that which was presented in the images included in the inspection report, providing
confidence that the two candidate UAVs could be used to support UAV-assisted inspection.
Findings and deliverables from this project will help NCDOT justify capital purchases made to support
UAV-assisted inspection, as well as additional research needed to integrate UAVs into their current bridge
inspection processes. The findings from this project particularly enables a systematic approach forselection and certification of UAVs for bridge inspection. Findings from this project directly support afollow-up project, which will focus on developing workflows to support UAV-assisted bridge inspection
and translation of UAV-obtained data into inspection reports. It is expected that adopting UAS-enabledbridge inspection will improve the quality of collected data through a stable flight system and enableconducting post-analysis of the acquired information for bridges’ health diagnostics and assessment of
their residual life.

5TABLE OF CONTENTSDISCLAIMER

3ACKNOWLEDGMENTS

4EXECUTIVE SUMMARY

5TABLE OF CONTENTS

6

LIST OF TABLES

9LIST OF FIGURES

10

1 INTRODUCTION AND RESEARCH OBJECTIVES
1.1
Introduction
1.2

Research Objectives

1
1
1

2 SUMMARY OF KEY LITERATURE FINDINGS

2

3 IDENTIFYING CANDIDATE COMMERCIALLY AVAILABLE UAVS FOR THE BRIDGEINSPECTION
3.1
Introduction

5
5

3.2

Background7

3.3

Methodology

9

3.3.1

Comparison and Description of Categories

9

3.3.2

Comparison and Description of Criteria

9

3.3.2.1 Flight Performance9

3.3.2.2 Situational Awareness10

3.3.2.3 Payload and Sensor Capabilities

10

3.3.2.4 Communication Quality10

3.3.2.5 Results

14

4 DEVELOPMENT OF COMPUTATIONAL FLUID DYNAMICS (CFD) MODELS OF UAVSFLYING NEAR, ALONGSIDE, AND WITHIN A BRIDGE
4.1
Background

19
19

4.2

Collection of Weather Data

19

4.3

CFD Methodology and Numerical Setup for UAV Flight Simulation

21

4.4

Flow Domain

23

4.5

CFD Methodology and Numerical Setup for Near-field Bridge Environment

28

4.6

Summary of findings

31

5 DESIGNING EXPERIMENTS TO TEST AND EVALUATE THE PERFORMANCE OF THE
UAVS FOR BRIDGE INSPECTION
5.1
Introduction

31
32

5.2

Experiment Design Process

32

5.3

RF Immunity Testing

32

5.3.1

RF Immunity test setup

33

5.3.2

Test procedure

34

5.4

Endurance Testing

34

6

5.5

Endurance test setup

35

5.6

Hovering mode endurance test procedure

35

5.7

Forward mode endurance test procedure

35

5.8

Vibration Testing

35

5.8.1

Vibration Test Setup

36

5.8.2

Vibration Test Procedure

36

5.9

Global Position System (GPS) Testing

36

5.9.1

GPS test setup

37

5.9.2

GPS test procedure

37

5.10

Illumination Testing37

5.10.1 Illumination test setup

38

5.10.2 Image Quality Assessment

38

5.10.3 Illumination test procedure

39

5.11

39

Autonomy Testing

5.11.1 Autonomy Test Setup

39

5.11.2 Autonomous waypoint-following procedure

39

5.11.3 Auto-land procedure

39

5.11.4 Return to Home Test Procedure

40

5.12

40

Maneuverability Testing

5.12.1 Maneuverability Test Setup

40

5.12.2 Maneuverability Test Procedure

40

5.13

40

Agility Testing

5.13.1 Agility Test setup

40

5.13.2 Agility Test Procedure

41

5.14

41

Wind Gusts Tolerance Testing

5.14.1 Wind Gusts Test Setup

41

5.14.2 Wind Gust Test Device Development

41

5.14.3 Wind Gusts Test Procedure

42

6 TESTING AND EVALUATION RESULTS FOR THE CANDIDATE UAVS
6.1
Introduction

43
43

6.1.1

RF Immunity

43

6.1.2

RF Interference impacts on command signal, GPS, Video-feed

43

6.1.3

RF Channel switching between default and redundant channel.

47

6.2

Endurance

47

6.2.1

Forward mode endurance testing

48

6.2.2

Hovering mode endurance testing

49

6.3

Global Position System

5076.3.1

GPS time-to-recover

50

6.4

Autonomy

50

6.4.1

Illumination

51

6.4.2

Vibration

54

6.5

Wind Gusts Tolerance

55

6.6

Reassessing the selection of the UAVs

56

7 FIELD TRIALS
7.1
Test flights at Structure 620072

57
57

8 IMPLEMENTATION AND TECHNOLOGY TRANSFER PLAN

62

9 CONCLUSION64

A APPENDICES
A.1
APPENDIX A – EXTENDED LITERATURE SURVEY ON TESTING AND
EVALUATION OF UAV-ENABLED BRIDGE INSPECTION

66
A-1

A.1.1

Introduction

A-1

A.1.2

Conventional Bridge Inspection Techniques

A-1

A.1.3

UAS-Enabled Bridge Inspection

A-3

A.1.4

Benefits of UAS-enabled bridge inspection

A-3

A.1.5

Challenges of UAS-enabled bridge inspection

A-4

A.1.6

Related works

A-4

A.2

REFERENCES

A-7

8LIST OF TABLES
Table 3.1: Defined criteria for selection of UAS-enabled bridge inspection.
Table 3.2: The scaling mechanism of pairwise comparisons within the AHP methodology
Table 3.3: AHP comparisons for categories.
Table 3.4: The technical details and Criteria values for candidate UAS platforms.
Table 3.5: AHP comparisons for Flight Performance criteria
Table 3.6: AHP comparisons for the Situational Awareness criteria
Table 3.7: AHP comparisons for Payload and Sensor criteria
Table 3.8: AHP comparisons for Communication Quality criteria
Table 3.9: Criteria sorted by their relative total weights
Table 3.10: AHP comparisons for candidate comparison with respect to each criterion.
Table 3.11: The criterion scores of candidates UASs and their final score
Table 4.1: Summary of weather station data.
Table 6.1: Sustained Noise RF immunity test results.
Table 6.2: Sweeping Noise RF immunity test results.
Table 6.3: Forward mode endurance test results.
Table 6.4: Hovering mode endurance test results.
Table 6.5: GPS time to recover.
Table 6.6: Autonomy test results
Table 6.7: Entropy of Images for Each UAV (Bridge Site)
Table 6.8: Vibration impact on PSNR
Table 6.9:Wind Gust Impact on Acceleration Change
Table 6.10: Scores of UAVs 1 and 2

9

6
8
10
11
13
13
13
14
14
17
17
21
45
45
48
49
50
50
54
55
55
56

LIST OF FIGURES
Figure 4.1: Location of wind speed and direction sensors within interior diaphragm at bridge. Note mast
containing data acquisition/transmission equipment and temperature/humidity sensors mounted near
exterior girder.
20
Figure 4.2: Location weather station mounted above the deck.
20
Figure 4.3 Wind speed (a) and wind gust (b) for the month of September 2020.
20
Figure 4.4: Wind speed (a) and wind gust (b) during Hurricane Elsa, July 8, 2021.
21
Figure 4.5: Photographs of the actual Skydio 2 UAV.
22
Figure 4.6: Developed Solidworks CAD model of the Skydio 2 UAV.
22
Figure 4.7. Fluid flow domain: (a) meshed drone body wall (b) UAV wall and rotating domains, and (c)global stationary region enclosed around the UAV and rotating domains.
24
Figure 4.8: Coarse mesh of rotating domain enclosing the propeller.
25
Figure 4.9: Velocity plots: (a) vector plots around rotating domains and (b) top view of UAV and velocity
contours of rotating domains.
26
Figure 4.10: Total Pressure at time 0.0015s
27
Figure 4.11: Plot of propeller performance parameters.
27
Figure 4.12: Solid model of structure 4.
28
Figure 4.13: Velocity contours through a series of diaphragms along the center (left image) and thequarter plane (right image) with the bridge geometry.
29
Figure 4.14 Velocity contours through a series of diaphragms along the center (left image) and the
maximum velocity (right image).
29
Figure 4.15 Turbulence kinetic energy contours through a series of diaphragms along the center (leftimage) and the maximum velocity (right image).
30
Figure 4.16: Velocity contours through a series of parallel to the diaphragms.
30
Figure 4.17: Velocity contours with increasing distance from the diaphragm.
31
Figure 5.1: Experiment design process.
32
Figure 5.2: RF test setup.
34
Figure 5.3: Pictorial view of RF experiment
34
Figure 5.4: A 25-pixel image.
36
Figure 5.5: GPS test setup: final state.
37
Figure 5.6:: GPS test setup: initial state.
37
Figure 5.7. Illumination test setup.
38
Figure 5.8. Setup for Wind Gust Experiment.
41
Figure 5.9: Block Diagram of Custom Inertial-Flow Sensor Hardware.
42
Figure 5.10: Custom flight data collection mounted on a UAV.
42
Figure 6.1 (a) : UAV control channel and environmental noise.
44
Figure 6.2 (b) : Sustained RF noise
44
Figure 6.3 (c): Sweeping RF noise.
44
Figure 6.4: Image quality of UAV 3 for RF noise <−17dBm.
46
Figure 6.5: Image quality of UAV 3 for RF noise =17dBm.
46
Figure 6.6: RF Channel switching between default and alternate band.
47
Figure 6.7: Image quality of UAV 3 after exposure to RF noise>−17dBm.
47
Figure 6.8(a): Illumination level 4 (> 250 lx)
51
Figure 6.9: Illumination Level 4
53
Figure 6.10: Illumination Level 3
53
Figure 6.11: Illumination Level 2
54
Figure 6.12: Illumination Level 1
54
Figure 7.1: Overview of structure 620072 in Moore County.
58
Figure 7.2 Debris pile from high flow event piled against column. Using imaging analysis technology, thevolume of the pile could likely be calculated to assist in debris removal activities.
59

10

Figure 7.3: Debris pile from high flow event piled against column. Using imaging analysis technology,
the volume of the pile could likely be calculated to assist in debris removal activities
59
Figure 7.4 Spalls on column, corroded longitudinal steel. At closer magnifications of the image, thedeformations on reinforcing bars are visible, as well as cracks alongside the spalled areas. Note thecondition of the channel bottom is also evident.
59
Figure 7.5: Cracks on pier over longitudinal steel. Staining evident, indicating initiation of corrosion. 59
Figure 7.6 Hairline cracks visible on underside of concrete deck.
60
Figure 7.7: Crack on underside of pier cap, following longitudinal steel. Discoloration and staining
evident from drainage.
60
Figure 7.8: Diaphragm with bolted and welded connections shown, along with condition of concrete piercap. Condition of bearing pad, plate, and bolts can also be observed.
60
Figure 7.9: Steel plates bolted onto pier cap, with corrosion evident. Condition of girder, diaphragms andconnections can also be observed.
60
Figure 7.10: Spall with exposed, corroded rebar in concrete diaphragm overhang. Condition of drainage
pipes and utility conduit can also be observed.
61
Figure 7.11: Staining on underside of girders.
61
Figure 7.12: Staining and cracking in pier cap. Condition of girders and diaphragm is also evident.
61
Figure A.1: Figure A-1. Scaffolds [78].
A-2
Figure A.2: Snooper trucks [79].
A-2
Figure A.3: Rope access [80].
A-2
Figure A.4: State DOTs with current or past UASs exploration.
A-3

11

1 INTRODUCTION AND RESEARCH OBJECTIVES
1.1 Introduction
There are about 13,500 bridges across the state of North Carolina (NC) highways. Impacts from trafficloads and natural deterioration and aging processes affect the condition of these bridges over the years. To
ensure compliance with the National Bridge Inspection Standards (NBIS) requirements, and to facilitate
planning of maintenance, repairs, rehabilitation, and replacement, the North Carolina Department ofTransportation (NCDOT) regularly inspects all bridges at least every two years. Bridges differ in structure
type based on (1) structural elements used, (2) what they carry, (3) materials used, (4) whether they are
fixed or moveable, and (5) the terrain characteristics (e.g. what they span, onshore/nearshore). Like allstates, North Carolina has a number of bridges that are classified as structurally deficient, and many bridges
that are exhibiting distress due to natural aging and service conditions [1]. Although these bridges are safe
to be used, inspections are required to ensure that their condition is known and the appropriate maintenance,
repair, and rehabilitation activities are performed to allow these bridges to remain in service. The National
Bridge Inspection Standards (NBIS) require all bridges require inspection every two years, with structurally
deficient and other bridges of concern often requiring a more aggressive inspection plan. In addition toregular inspections, there are ad-hoc occasions that the bridges should be inspected for example after aflood or hurricane to ensure that they can be safely opened to the public.
The current inspection practices heavily rely on traditional approaches through which a team of
specialized and certified inspectors conduct visual field inspections, sometimes in extremely difficult andrisky conditions. It requires the inspectors to either climb and rappel or use mobile lift equipment (bucket
lifts or “snooper trucks”) or specific mobile assemblies to look for small defects and cracks in areas thatare difficult to access or are at a high elevation from the ground below. Through this process detected
deteriorations are manually documented, recorded, measured, and photographed. This manual process isoften time consuming, and inaccuracies and variability can occur due to unavoidable human bias,
experience, fatigue or errors. The information is recorded in the form of written reports and/or individual
photos, which makes it difficult for agencies to employ advanced data-analytic techniques for postprocessing and analysis of the detected problems. The American Society of Civil Engineers (ASCE)
report, released in 2020, emphasizes the requirement of implementing more efficient and cost-effective
alternative solutions for bridge inspection.
Considering the significant number of bridges across NC highways with a need for regular and ad-hoc
inspections, particularly for those bridges that are structurally deficient, a more advanced bridge inspection
approach is needed to facilitate the current manual inspection processes. Unmanned aerial vehicles(UAVs) provide a solution for this problem by carrying the sensing equipment around the bridges.
However, commercially available UAVs cannot be directly used for bridge inspection, as the highly
turbulent environment around the bridges could lead to the loss of control of drones under unfavorable
conditions. Therefore, there is a need to develop test and evaluation techniques to assess the technologicalreadiness level of commercially available UAVs for bridge inspection. In addition, there is a need for abetter understanding of the near-bridge flow field which in turn impacts the robustness of the flightcontrollers to handle wind gust disturbances around the bridges.
1.2 Research Objectives
This multidisciplinary project develops test and evaluation techniques for candidate UAVs, explores andevaluates the complex nature of flying UAVs in close proximity to bridges, and assesses the limitations of
UAV flight around bridges due to turbulent flows around bridge components and nearby terrain.
There have been some efforts to use general-purpose commercially available UAVs for bridge1inspection. However, the turbulent environment that often exists around bridges likely requires customized
and enhanced UAV platforms with a higher level of robustness, taking into account the bridge type andstructure as well as the weather conditions around the bridge. Additionally, the data-acquisition
capabilities of commercially available UAVs need to be compared to those required for bridge inspection.
Currently, there is no study to quantify the gap between the performance of the commercially available
UAVs and ideal desired performances. To address this concern, there is a need to develop metrics tomeasure the performance of the UAVs for bridge inspection and design experiments to test and evaluate
the UAVs by pushing them to the edge of their performance envelope. Further, near-bridge environments
include irregular air motions characterized by winds that vary in speed and direction. These chaotic
property changes, including low momentum diffusion, high momentum convection, and rapid variationof pressure and flow velocity in space and time determine the turbulence level of the area of interest. In
addition to the chaos, diffusivity, flow rotation, three-dimensional asymmetric flow behavior, anddissipation are observed in a turbulent flow. Diffusivity enhances mixing and the increased rate of mass,
momentum, and energy transport. The interaction among eddies of various scales passes energy
sequentially from the larger eddies gradually to the smaller ones, known as turbulent energy cascade. The
flow rotation and three-dimensional asymmetric flow behavior are associated with vortex stretching thatdefine the turbulent energy cascade size. There have been relatively few studies on the near-bridge
turbulence behavior. However, existing computational and experimental works show the importance of
this phenomena in different areas, including drone-based bridge inspection, bridge design, and bridge
aerodynamics.
To address these challenges and research needs, the goal of this project is to test and evaluate theperformance of commercially available platforms for bridge inspection by achieving the following
objectives:●●●

Objective 1.1: Develop metrics to measure the performance of the UAVs for bridge inspection.
Objective 1.2: Design experiments to test and evaluate the performance of UAVs for bridge
inspection.
Objective 1.3: Test and evaluate the performance of some commercially available UAVs for thebridge inspection.

Remark: To avoid commercial advertisement and biasing toward any of the participant UAVs, we do notcompare the performance of the UAVs with each other, and instead, we refer to the participant UAVs as
UAV 1, UAV 2, UAV 3, and UAV4.
2 SUMMARY OF KEY LITERATURE FINDINGSThe 2021 bridge infrastructure data and reports show that there are 617,000 bridges in the United States,
which play an important role in the transportation network. Among them, around 42% were built more than
50 years ago [1]. All bridges, particularly those that are aging, are subjected to natural deterioration.
Therefore, in order to maintain the bridges’ structural integrity and safety, regular inspection of bridges isrequired. Such practices obtain the required information about the health status of bridges for predictive
and preventive maintenance. In total, as of 2019, around 231,000 bridges across the United States require
repair and preservation work, even though they are still safe to be used. These bridges require moreaggressive inspection plans and extensive maintenance schedules to remain in service.
Historically, bridge inspection has been performed by humans using a variety of access techniques,
observation tools, and handheld equipment. Although human-based bridge inspection has served as thefoundation of bridge management and safety assessment, conventional bridge inspection techniques areless efficient and can sometimes expose inspection personnel to danger and are subject to inefficiencies anderror. To accomplish the required inspection tasks, inspectors rely on equipment including ropes, ladders,2lift trucks, and under-bridge inspection vehicles, which can be costly, often impact traffic flow, and may beviewed as risky to both inspectors and the travelling public from a safety perspective. Traffic control isoften required, and travel lanes can be restricted during inspection activities, impacting the traveling public
for significant durations. Additionally, the documentation and measurement process used by inspectors canbe subject to human errors, bias, and limitations due to accessibility and visibility. Inspection personnel
typically use textual descriptions and photos to document observed defects. The outcome of these recorded
defects relies heavily on the experience of inspection personnel and can also be easily affected by theexperience level or bias mood of the inspectors. In view of the challenges with the traditional inspection
techniques, it is important to explore alternative inspection means or tools that can supplement the existing
methods. UAVs and other types of mobile data acquisition systems have been identified seem to be a viable
tool that can mitigate most of these challenges.
In [2], a mobile manipulator imaging system was employed for bridge inspection in which two CCD(charged-coupled device) cameras are installed to replace manual inspection. This solution addresses thesafety and sensing-payload carrying issue, but it still suffers from traffic jams and logistic problems. These
problems are addressed in [3] by making the carrier vehicle smaller and more agile, but it is limited toconcrete bridges and still is too large to be used for train bridges [4]. The authors of [4] have improved thesize of the vehicle; yet their design still has inherent mobility issues. An alternative solution to the above
bridge inspection practices is to employ an Unmanned Aerial System (UAS) as a mobile sensing platform
for bridge inspection. Employing UAS for inspection can significantly reduce the work accident risks,inspection cost and time, and required logistics, while enhancing the quality and reliability of the collected
inspection data.
UAVs are of great interest to state highway agencies (SHA) to support a variety of activities, including
surveying, emergency response, education and outreach, construction inspection, and condition inspectionof a variety of infrastructure components [5, 6, 7, 8]. As UAV and supporting flight control and sensor
technologies are rapidly improving, and are available at lower costs, many SHAs are establishing andsupporting UAV programs to support a variety of objectives [9]. UAV-assisted bridge inspection is
becoming increasingly enticing to many SHAs. All publicly owned in-service bridges must be inspected
every two years, in accordance with the National Bridge Inspection Standards (NBIS) (23 CFR Subpart C).
Inspection procedures are outlined in the AASHTO Manual for Bridge Evaluation (MBE) [10] and theBridge Inspector’s Reference Manual [11] and have historically relied upon visual and manual techniques
performed by human inspectors.
Currently available UAV and sensor technologies have been found to be capable of supporting bridge
inspection activities, providing improvements in access, reductions in traffic restrictions and cost,
improvements in efficiency and safety, reduced environmental impacts, and enhanced data to support
higher quality end inspection end products to support decision making [12]. UAVs have been targeted tosupport several types of bridge inspections, including initial/inventory, routine, in-depth, fracture-critical,
special, and damage inspections. Specific inspection activities that can be supported by UAVs include [9]:•Capturing images of areas and elements that are challenging or impossible for human access.

•Capturing data to support development of a 3-D model, supplementing 2-D photographs typicallyused to document structure conditions.

•

Acquiring images and other data in a manner that can reduce human error and bias and can be used
to monitor progression of deterioration over time.

The utility of a UAV for bridge inspection is a function of both its platform and its sensors. Most UAV
currently used for bridge inspections are multirotor aircraft (not fixed wing aircraft) with a variety of sizes,

3flight time capabilities, and sensor technologies. Operation of UAVs is governed by the requirements
outlined in federal statutes 49 USC § 40102 and § 40105, and UAV operators are subject to FAA
regulations. UAVs less than 55 pounds are subject to regulations outlined in 14 CFR Part 107, withadditional requirements for UAVs greater than 55 pounds presented in 14 CFR Part 91. UAV operators
must also comply with requirements associated with the airspace in which they plan to fly the UAV in
Flying of UAV in the National Airspace System (NAS) requires compliance with a number ofrequirements, with FAA providing guidance at their website [9].
UAV can carry new miniaturized sensing technologies such as high-performance cameras, lidars, andthermal sensors which provide higher quality input to the state-of-the-art processing technologies such as
Deep Learning [13, 14]. UAS has a significant potential in different transportation and civil engineering
applications including but not limited to building inspection [15, 16, 17], transportation infrastructure
assets [18, 19, 20], traffic surveillance [21, 22], safety inspection [23, 24], building facades inspection[24], surveying earthwork [26], and construction site monitoring [27, 28, 29]. Using UAS for bridge
inspection is relatively new and sparse [30, 31, 32]. The application of UAS for inspection of GluedLaminated Timber Arch Bridges is investigated in [33]. The ceiling effect for Contact-Based bridge
inspection is studied in [34]. The employment of UAS for inspection of bridge beams is studied in [35].
A successful UAS-enabled bridge inspection procedure should address several challenges due to the UAS
limitations (e.g., poor situational awareness due to poor visibility), bridge structure constraints (e.g.,complex structure, turbulent flow fields around the bridge), environmental constraints (e.g., temperature,humidity), and site constraints (e.g., terrain characteristics). One of the recent efforts in the deployment of
UAS for bridge inspection in the US is carried out by the Minnesota Department of Transportation [36].
While the project was not continued due to several flight crashes, one valuable outcome of the effort was
highlighting the importance of understanding the required UAS specifications, considering the bridge andsite limitations for a successful inspection. To the best knowledge of the authors, currently, there is not
any verified comprehensive UAS-enabled solution for bridge inspection. Most existing case-dependentpractices, including the work in [36], rely on general-purpose commercially available UAS platforms.
There are currently a considerable number of general-purpose UAS commercially available, yet many
new designs are continuously being introduced to this competitive market. The challenge is then to choose
the best solution from the general-purpose UAS platforms for the specific application of bridge inspection.
This project explored UAVs feasibility for bridge inspection. UAVs are flown remotely either inautonomous mode or in manual mode by a pilot with the help of a controller. In the past, they were usedby the military for combat and reconnaissance missions. However, in recent times, they have been used
in applications such as, remote sensing [25], search and rescue missions [26], disaster management [27],courier services [28], security and surveillance [29], wireless coverage [30], precision agriculture [31],and infrastructure inspection [32]. The main drivers for the popularity are advances in UAV technologies
and in remote sensing technologies such as light detection and ranging (LiDAR) and photogrammetry. In
fact, bridge inspections seem to be the one application area that is receiving attention due to cost and safetyadvantages they offer. Use of UAV as a tool to supplement conventional bridge inspection methods will
potentially provide a number of advantages. It can eliminate the risk associated with rope access andsignificantly lower the risk of access using other methods. In addition, it will provide cost savings, sinceuse of specialized equipment such as snooper trucks, scaffolds, and man lifts can be reduced or eliminated.
Use of UAVs will also provide time savings, reduce the amount of time bridge inspectors are at risk, andreduce traffic impacts. Furthermore, they offer real-time bridge inspection by means of mirroring the pilot
screen onto another screen which can be viewed by inspectors. The inspectors can thus take a snapshot of
identified defects for further examination later. Also, the remote sensing technologies for bridge
inspections are non-contact techniques that lead to minimal traffic disruption. Finally, the collected data
is a soft-copy and thus it makes the easy application of advanced data analysis techniques.
The main challenge that restricts the use of UAVs for bridge inspection is government regulations. For

4example, Federal Aviation Authority (FAA) regulations forbid UAV flying on traffic or persons notdirectly involved in the inspection. Also, there is also the restriction of beyond-line-of-sight flight [17].
The UAV capability to operate in a GPS-denied environment [37], bad weather [33] and its flight duration
also limits viability for bridge inspection. The small size of the UAV makes them susceptible to wind
gusts rendering them difficult to control during inspection [33]. In addition, some bridge inspection tasks
requiring physical contact, such as determining the width of cracks, cleaning bridge elements to remove
rust or dust for detailed defect observation cannot be accomplished with UAVs [33]. Finally, there is noavailable comprehensive guide on testing the various UAV criteria for a specific task. This is particularly
important for inspectors to have a fair idea of how the chosen UAVs will perform when used forinspection.
There are some existing studies that have explored the viability of UAVs for bridge inspection. For
instance, [17] investigated UAV-enabled bridge inspection methodology which led to the development ofa five-stage bridge inspection workflow: bridge information review, site risk assessment, pre-flight setup,
UAVs-enabled inspection and data processing. The main criteria for UAV selection were based on flighttime, upward viewing camera capability, camera resolution, video resolution, payload capacity,
illumination and communication range. Minnesota DoT in collaboration with Collins Engineering
reported their findings on the challenges and benefits of UAVs for bridge inspection [34]. This work was
carried out using four different bridges in order to evaluate the effectiveness of the UAVs for inspecting
different types of bridges. The Michigan Department of Transportation also explored UAV-enabled bridge
inspection using 5 different UAVs for bridge inspection [35]. This work also highlighted the effectivenessof a particular UAV as a supplementary tool for bridge inspection.
The previous studies that explored UAVs for bridge inspection placed less emphasis on the UAV
selection. In most cases no test was conducted by the authors to verify if the UAV can withstand bridge
conditions. As a result of the sole reliance on information in the specification documents, important criteriasuch as RF immunity and GPS robustness is rarely taken into consideration during UAV selection.
Therefore, in this work, a comprehensive list of important UAV criteria for bridge inspection is presented.
In addition, experiments are developed to test these features.
3 IDENTIFYING CANDIDATE COMMERCIALLY AVAILABLE UAVS FOR THE BRIDGEINSPECTION
3.1 Introduction
Considering the enormous number of bridges in the United States, inspection process improvements havea significant impact on public safety, transportation system effectiveness (i.e., availability), andmaintenance/repair/rehabilitation/replacement procedures (i.e., scheduling, cost, and impact to thetraveling public) of bridges. As our nation’s highway infrastructure system ages and expands toaccommodate a growing population, the resources required to build, maintain, and inspect our bridge
inventory will increase. Despite the growing demand for the regular inspection of bridges, the current
practices heavily rely on traditional approaches through which a team of specialized and certified
inspectors conducts visual field inspections, sometimes in extremely difficult and risky conditions. Thismanual process is time-consuming and inaccurate due to unavoidable human errors (e.g., the level ofworkers fatigue and experience). Further, this manual procedure reduces the possibility of accurate
multimodal sensing documentation for offline post-inspection processes due to the limited amount ofequipment an inspector can carry. These manual and labor-intensive inspection procedures often lead totraffic-flow (e.g., when an inspection vehicle blocks lanes, or lanes are closed to traffic to protect
inspectors on foot) interruptions and introduces unnecessary safety risks to both inspector and the public.
Therefore, innovative bridge inspection practices, as an enabling step of any maintenance planning, havebeen of interest to researchers to evaluate the feasibility of deploying emerging technologies [38].

5This report addresses the existing research gap of selecting a suitable UAS platform for a bridge
inspection mission. A systematic framework for ranking available UAS candidates is developed based on
three key factors: (1) UAS capabilities and limitations, (2) UAS sensing capabilities, and (3) UAS reaction
to environmental parameters. An Analytical Hierarchy Process (AHP) methodology is adopted for themultiple-criteria decision-making (MCDM) process. In this hierarchical method, four major categories
including Flight Performance, Situational Awareness, Payload and Sensor Capabilities, andCommunication Quality are defined. Within Each category, multiple criteria are defined and compared
pairwise. This AHP method results in a sorted list of criteria which are more important for selection of
UASs for bridge inspection. Then, the outcome of AHP is applied to a set of commercial UASs, and thecomparison result is provided for three UASs.
Table 3.1: Defined criteria for selection of UAS-enabled bridge inspection.
Parameter

Explanation

Flight Performance
Endurance

Maximum flight time with one fully charged battery (minutes)

Wind Tolerance

Maximum wind speed in which the UAS can operate safely(meters/seconds)

Gusts Tolerance

Maximum wind gust in which the UAS can operate safely(meters/seconds)

Max Altitude

The highest altitude UAS can reach (meters MSL)Net Weight

The weight of the functional UAS excluding its sensors (kilograms)

Environmental
Temperature Range

The temperature range of the environment in which the UAS can safely
operate (Centigrade)

Autonomy

The capability of operating autonomously with minimum humaninteractions

Number of Engines

The number of engines UAS is equipped with

Max Climb Rate

Maximum vertical speed UAS can achieve (meters/seconds)

Max Airspeed

Maximum horizontal speed UAS can achieve with respect to the air(meters/seconds)

All-weather ability

The capability of operating in different weather conditions

Physical Protection

The cage, ring, shroud, or similar measures installed to reduce the riskof damage caused by propellers in case of a crash

Situational Awareness
NavCamThe capability of perceiving the surrounding area visually throughNavigational Cameras

6

GNSS

The accuracy of on-board equipment to utilize Global Navigation
Satellite Systems

Ultrasonic

The capability of perceiving the surrounding area and measuring
distance to obstacles using ultrasonic sensors

Gyro

The accuracy and update rate of the installed gyro

Accelerometer

The accuracy and update rate of the installed accelerometer

Compass

The accuracy and update rate of the installed compass

Magnetometer

The accuracy and update rate of the installed magnetometer

Barometer

The accuracy and update rate of the installed narometer

Beyond Line-Of-Sight
Range

The radius in which UAS can safely operate without being in the line ofsight of pilot or human supervisor (meters)

RTK

The accuracy and update rate of the installed Real Time Kinematics

Payload and Sensor Capabilities
Can look upward?

Capability of mounting sensor to look upward

Can carry custom
payload?

Capability of carrying custom payloads

Max payload weight

Maximum weight UAS can carry (kilograms)

Default payload

The default sensor

Communication Quality
Frequencies

Frequencies UAS is using for both RC and streaming data

Low Latency

The delay of streamed data

Live Stream

Capability of streaming sensor data online

Communication Range

The maximum distance UAS can send data (meters)

RC Range

The maximum distance UAS can be controlled using Remote Controller(meters)

Encryption

Capability of Encrypting Communication channels

3.2 Background
Multiple-criteria decision-making (MCDM), also known as Multi-Criteria Decision Analysis (MCDA), isan approach towards the decisions involving the choice of the best alternative among several potential
candidates by explicitly evaluating multiple conflicting criteria [39]. In MCDM, which is a field of7

operational research, potential candidates are assessed to select the most suitable alternative which satisfies
the desired goal with respect to the set of multiple and often conflicting criteria, e.g., customer satisfaction
and the cost of providing service in the service industry [37, 40].
Different scenarios have different degrees of risk, fuzziness, and uncertainty; therefore, different
methods of MCDA are developed to be compatible with different scenarios. The Simple Multi-Attribute
Rating Technique (SMART) [41], SWING weighting [42], TRADEOFF weighting [43], and Analytical
Hierarchy Process (AHP) [44] are some of the more well-known MCDA methods in the literature. Amongthese methods, AHP has been a popular method for MCDM applications including but not limited tosustainable energy planning [45], wind farm site selection [46], bidding decision making [47],maintenance planning of reinforced concrete [48], post-disaster sustainability analysis [49], classificationof areas suitable for fish farming [50], and safety evaluation of urban public parks [51].
AHP employs a hierarchical weighting mechanism, where each layer conducts a comparison between
each pair of nodes [44]. From the top to the bottom layers of an AHP hierarchical structure, the layers
include nodes of goal, categories, criteria, or candidates/alternatives. The weighting mechanism within the
AHP technique is conducted based on the values provided in Table 3.2 to assist judgements (pairwise
comparisons between nodes). These judgments are conducted to convert quality to quantity and establish
a partial order relation based on the pairwise comparison over a set of nodes. After establishing thesepairwise relations, the relative weight of each component in the table can be found. The inconsistency of
judgments (violation of transitivity or anti-symmetric relations) can be numerically captured via theconsistency ratio, CR, which is calculated over the reciprocal matrices containing the results of thejudgments. The consistency ratio CR is related to the largest eigenvalue 𝜆𝑚𝑎𝑥 in the reciprocal matrix. To
𝜆−𝑛mathematically define CR, [44] introduced the consistency index CI as 𝐶𝐼 = 𝑚𝑎𝑥
where n is the𝑛−1number of nodes we are comparing (the number of rows/columns of the reciprocal matrix). Then, theconsistency ratio CR can be defined as:

𝐶𝑅 =

𝐶𝐼
𝑅𝐼

Where RI is the random consistency index that provides the average CI of randomly generated
comparisons [44]. The comparison matrix might be reconstructed if the CR value exceeds a certain
threshold, e.g., 10%.
Table 3.2: The scaling mechanism of pairwise comparisons within the AHP methodology
Intensity ofImportanceDefinition

Explanation

1

Equal importance

Two activities contribute equally to the objective

3

Moderate importance of one
over another

Experience and judgment slightly favor one activity
over another5

Essential or strong importanceExperience and judgment strongly favor one activity
over another7

Extreme importanceAn activity is favored very strongly, and its dominanceis demonstrated in practice89Extreme importance

The evidence favoring one activity over an-other is of
the highest possible order of affirmation

2,4,6,8Intermediate values

When compromise is needed

3.3 Methodology
The goal of the deployed AHP in this research study was to select suitable UAS characteristics that can best
conduct bridge inspection. In the hierarchical structure of the deployed AHP, four categories were used.
Within each category, multiple criteria were identified. The list of all categories and their associated criteria
are listed in Table 3.1. This section will provide a description and pairwise comparison of the categories
and their associated criteria. Then, the candidate UASs are compared against each other with respect toeach criterion. A detailed description of the categories, criteria, candidate UASs, and their pairwise
comparison matrices are provided in the next subsections.
3.3.1

Comparison and Description of Categories

In the hierarchical structure of the deployed AHP, four categories were used, including Flight Performance,
Situational Awareness, Payload and Sensor Capabilities, and Communication Quality:●●●●

Flight Performance category refers to flight characteristics of UASs in terms of physicalproperties, flight dynamical properties, and autonomy capabilities of UASs
Situational Awareness category concerns about how well a UAS can perceive the surrounding
environment.
Payload and Sensor Capabilities category reflects the sensing features and the potential forcarrying the required sensors; andCommunication Quality category represents the factors contributing to the quality ofcommunication links.

The list of all categories and their associated criteria are listed in Table 3.1. The pairwise comparisonof these categories is provided in Table 3.3. For example, Flight Performance is extremely more important
to consider for the selection of a UAS than Communication Quality, as most UASs are equipped withrelatively reliable long-range communication devices which is sufficient for bridge inspection, where thepilot/ground station is relatively close to the inspection site. Therefore, using the guideline provided inTable 3.2. Other elements of the table are filled with similar reasoning, based on experts’ opinion and theavailable literature. Note that Table 3.3 only shows the upper triangular components, understanding thatthe lower part can be obtained as the inverse of the components that are symmetric with respect to thematrix diagonal.
3.3.2

Comparison and Description of Criteria

3.3.2.1 Flight PerformanceThis category contains parameters such as the flight performance of UASs including flight and dynamical
characteristics of UASs, autonomous flight performance capabilities, and physical properties of UASs thatare important for the selection of UASs for the bridge inspection purpose. The list of criteria under thiscategory and their description is provided in Table 3.4. The pair-wise comparison for the criteria under
Flight Performance category is provided in Table 3.5. For example, Gusts Tolerance is a very importantfactor for a bridge inspection mission because even smallest amounts of wind can interact with the structureof the bridge, resulting in areas with turbulent air mass powerful enough to impact the physical behavior of

9

UAS flying close to the bridge [52, 53, 54]. Therefore, Gusts Tolerance has a relatively higher value(intensity of importance) compared to other criteria.
3.3.2.2 Situational AwarenessFor bridge inspection, good autonomy results in more stable flights. It provides the opportunity forcollecting higher quality and more consistent data. However, higher levels of autonomy require moreadvanced situational awareness systems. On the other hand, in aviation, safety is a crucial factor [55] whichcannot be achieved without accurate and comprehensive situational awareness [56]. In general, manned
aviation, situational awareness includes details of the aircraft’s operational parameters, external conditions,navigational information, the status of the aircraft, and hostile factors [57]. Higher, or at least the same,
expectations are required for an (autonomous or piloted) unmanned aircraft. This is due to the fact that
despite all arrangements to meet the general requirement of maintaining constant line of sight, enforced byFederal Aviation Administration (FAA) [58], due to the structure of the bridge, the line of sight might beinterrupted, where a proper situational awareness is critical in such a challenging environment. Situationalawareness is generally obtained by processing the data gathered from (onboard) sensors. These sensors canbe different from the main sensors which are supposed to acquire data for the bridge inspection mission,and include Navigational Cameras (NavCam), Global Navigation Satellite System (GNSS); Real-time
kinematic (RTK) positioning, gyro, accelerometer, compass, magnetometer, barometer for accurate
determining the position, orientation, and acceleration of the UAS, as well as the range that the UAS may
travel autonomously beyond the line of sight. The comparison of these criteria for assessment of situationalawareness is given in Table 3.6. and 3.7.
3.3.2.3 Payload and Sensor Capabilities
The goal of a bridge inspection mission is to gather data employing specific sensors. Some aircraft are
equipped with a sensor that cannot be changed while others are capable of carrying custom sensor payloads.
While the default sensor of the aircraft is important, the maximum weight of the custom payload is also adecision parameter for selecting an aircraft for bridge inspection. One of the main advantages of using UAS
for bridge inspection is the possibility to easily gather data from surfaces that are difficult to access withother approaches. Such surfaces might be located beneath certain bridge elements (such as the underside ofa bridge deck), or in hard to access areas (such as the bearings of girders). Therefore, in addition to being
able to collect data in lateral and downward directions (which is a common capability of almost all generalpurpose UASs), it is important that the UAS be able to look upward and rotate to gather data for bridge
inspection. These considerations about payload and sensor capabilities are included under Payload andSensor Capabilities category and are compared in Table 3.7.
3.3.2.4 Communication Quality
A UAS may communicate with the Ground Control Station for two purposes: (1) to receive control andcommand signals via the radio link, and (2) to transmit sensor readings. The communication must be done
via reliable frequency channels. For the radio link, the ground human operators need to use long-range
remote controllers (RC). Further, it is important to send encrypted and low-latency control and commandsignals to the UAS. On the other hand, for the data link, it is important to live-stream large volumes of data
(e.g., video feed) as the on-board storage usually is not sufficient to save the entire mission data. These
concerns are included in the Communication Quality category and have been compared under Table 3.8.
Table 3.3: AHP comparisons for categories.
FlightSituational Payload & Sensor Communication

10

WeightPerformance AwarenessFlightPerformance

1

Situational
Awareness

Capabilities

Quality

2

1

9

0.389

1

1

9

0.281

1

5

0.287

1

0.043

Payload &SensorCapabilities
Communication
Quality

Table 3.4: The technical details and Criteria values for candidate UAS platforms.
Parameter

UAV 2

UAV 1

UAV 3

UAV 4

23

16-26

5

11

12-16

5

Flight Performance
Endurance (mins)

25

Wind Tolerance
(m/s)

13.5

Max Altitude (m
MSL)

4500

4572

4000

N/A

Net Weight (kg)

0.32

0.775

1.2

2.69

Environmental
Temperature Range(centigrade)-10 to 40-5 to 40

-5 to 40

-20-40Autonomy

Yes

Yes

Yes

No

Max Climb Rate
(m/s)

4

4

10

5

Max Airspeed (m/s)

15.3

16

10

Pilot dependent

All-weather ability

No

No

No

No

Physical Protection

No

No

Yes

No

Number of Engines

Situational Awareness11

NavCam

N/A

360

N/A

1 FPV

GNSS

GPS&
GLONASS

AGPS

GPS, GLONASSGPSUltrasonic

N/A

N/A

N/A

N/A

Gyro

N/A

31 milli rad/s @
8KHz

N/A

N/A

Accelerometer

N/A

88_g@4kHz

N/A

N/A

Compass

N/A

N/A

N/A

N/A

Magnetometer

N/A

N/A

N/A

N/A

Barometer

N/A+/- 0.1 hPa;
25Hz

N/A

N/A

Beyond Line-OfSight Range

Battery or
missiondependent

3500

Yes

N/A

RTK

N/A

N/A

N/A

N/A

Payload and Sensor Capabilities
Can look upward?

Yes

Yes

Yes

Yes

Can carry custom
payload?

Yes

No

No

No

Max payload weight
(kg)

N/A

0.1

0.8

0.5

Sony Alpha 7R

Sony EOXII
and FLIR
Boson 320

Default payload

3axis gimbal,
4k
Color
Camera/Therm
Camera - 1/2.3"
al
12.3MP CMOS

Communication Quality

Frequencies

2.4GH (2xWi-Fi
2.4-2.483; 5.18Diversity
2.4 GHz, 5.8
802.11a/b/g/n
5.24;
control/data links),GHz
2.4 - 5.8 GHz 5.725-5.85 GHz
5.8 GHz

Low Latency

Yes

Yes

Yes

Yes

Live Stream

Yes

No

Yes

Yes

Communication
Range (m)

4000

3500

1000

6920

12

RC Range (meters)

4000

3500

1000

6920

Encryption

WPA2

Yes

Yes

Yes

Table 3.5: AHP comparisons for Flight Performance criteria

Table 3.6: AHP comparisons for the Situational Awareness criteria

Table 3.7: AHP comparisons for Payload and Sensor criteria

Can look upward?
Can carry custom payload?

Can
look
upward?

Can
carrycustompayload?

Max payload
weightDefault
payloadWeights

1

7

9

7

0.702

1

1

1

0.095

1

1/3

0.072

1

0.131

Max payload weight
Default payload

13

Table 3.8: AHP comparisons for Communication Quality criteria

3.3.2.5 Results
Table 3.3 provides the pairwise comparison of the categories as explained in Section 2. The relative weights
are provided in the weight column of Table 3.3. A similar procedure is applied to find the relative weightsof criteria in Flight Performance, Situational Awareness, Payload and Sensor Capabilities, andCommunication Quality categories, shown in the weight column of Tables 3.5, 3.6, 3.7, and 3.8,
respectively. From the relative weight of the criterion in each category, the overall relative weight of eachcriterion, the Total Weight column in Table 3.9, can be calculated by multiplying the criterion weight by
the weight of the category to which the criterion belongs to, i.e., Category Weighting-category Weightcolumns. Table 3.9 contains the criteria sorted by their relative total weights. As can be seen from Table
3.3, the Flight Performance category has the highest weight, followed by Payload and Sensor Capabilities,
Situational Awareness, and Communication Quality. The overall results in the compiled Table 3.9 follows
a similar pattern, where the top half of the table is mainly dominated with criteria within the Flight
Performance category. Yet, some criteria have been elevated or demoted by the hierarchical mechanism of
the AHP. For instance, the importance of the capability of looking upward and collecting data from surfaces
which are placed beneath the bridge has elevated the weight of the Can look upward? criterion and placed
it at the top of the table. On the other hand, the candidates are compared and their relative weights withrespect to each criterion are shown Underweight columns of Table 3.10
Table 3.9: Criteria sorted by their relative total weights

Criterion

Category
WeightIncategory
Weight

Total
Weigh

Category

Can look upward?

0.287

0.702

0.2015

Payload and Sensor

Gusts Tolerance

0.389

0.238

0.0926Flight Performance

Autonomy

0.389

0.206

0.0801Flight Performance

Gyro

0.281

0.208

0.0584Situational Awareness

RTK

0.281

0.185

0.0520Situational Awareness14

Ultrasonic

0.281

0.172

0.0483Situational AwarenessAccelerometer

0.281

0.166

0.0466Situational AwarenessWind Tolerance

0.389

0.102

0.0397Flight Performance

Endurance

0.389

0.098

0.0381Flight Performance

Default Payload

0.287

0.131

0.0376

Payload and Sensor

Physical Protection

0.389

0.086

0.0335Flight Performance

All-weather ability

0.389

0.080

0.0311Flight Performance

Custom payload?

0.287

0.095

0.0273Payload and Sensor

Max Payload
weight

0.287

0.072

0.0207

Payload and Sensor

NavCam

0.281

0.073

0.0205Situational Awareness

Net Weight

0.389

0.051

0.0198Flight Performance

Number of Engines

0.389

0.048

0.0187Flight Performance

GNSS

0.281

0.060

0.0169Situational AwarenessCompass

0.281

0.054

0.0152Situational Awareness

Environmental
Temperature
Range

0.389

0.038

0.0148Flight Performance

Communication
Link Range

0.043

0.326

0.0140Communication Quality

RC Range

0.043

0.326

0.0140Communication Quality

Magnetometer

0.281

0.038

0.0107Situational Awareness

Max Airspeed

0.389

0.019

0.0074Flight Performance

Max Climb Rate

0.389

0.017

0.0066Flight Performance

Barometer

0.281

0.023

0.0065Situational Awareness

Max Alt.

0.389

0.016

0.0062Flight Performance

Live stream

0.043

0.134

0.0058Communication Quality

Beyond-Line-OfSight Range

0.281

0.020

0.0056Situational AwarenessFrequencies

0.043

0.112

0.0048Communication Quality

15

Low latency

0.043

0.074

0.0032

Communication Quality

Encryption

0.043

0.029

0.0012

Communication Quality

Verified by the consistency ratio, the results under the category, criteria, and candidate tables are
consistent. The CR values for Category table, Table 3.3, is 4.8%, and for the criteria within Flight
Performance, Situational Awareness, Payload and Sensor Capabilities, and communication Quality are
9.4%, 9.9%, 5.8%, and 2.5%, respectively; all of them are less than 10% as the defined threshold. Further,
the sub-tables for candidates’ comparison in Table 3.10 are all less than 10% as well.
With these consistent tables, the final score of each candidate UAS can be calculated, which shows thenumerical representation of candidate’s suitability with respect to the bridge inspection. the final score ofeach candidate UAS is calculated as:
𝑆 = ∑ 𝑠𝑖 𝑤𝑖𝑖where is the candidate’s score for the ith criterion, extracted from the Score columns of Table 3.10,
and 𝑤𝑖 is the total weight of that criterion, extracted from the Total Weight in Table 3.9. The criterion
score of each candidate and its final score are presented in Table 3.11 As it is shown in the last row ofTable 3.11, the final score of candidates A, B, C and D are 0.2549, 0.2577, 0.2743, and 0.2345,
respectively; the candidate C is the most suitable choice.

16

Table 3.10: AHP comparisons for candidate comparison with respect to each criterion.

Table 3.11: The criterion scores of candidates UASs and their final score
UAV 2

UAV 3

UAV 1

UAV 4

Criterion
WeightCan look upward

0.05040.05040.05040.05040.2015

Gusts Tolerance

0.02720.03270.02720.0272

0.0926Autonomous

0.02510.02510.02510.00500.0801

Gyro

0.01460.01460.01460.0146

0.0584Ultrasonic

0.00600.00600.03020.00600.0483

Accelerometer

0.01170.01170.01170.01170.046617

Wind Tolerance

0.01080.01450.01080.00360.0397

Endurance

0.01910.00380.01140.00380.0381

Defualt Payload

0.00870.01450.01160.00290.0376

All-weather ability

0.00780.00780.00780.00780.0311

Custom payload?

0.00680.00680.00680.00680.0273

Max Payload
weight0.00520.00520.00520.00520.0207

NavCam

0.00510.00510.00510.00510.0205

Net Weight0.00500.00500.00500.00500.0198

Number ofEngines0.00470.00470.00470.00470.0187GNSS

0.00420.00420.00420.00420.0169Compass

0.00380.00380.00380.00380.0152

Environmental
Temperature
Range

0.00190.00370.00190.00740.0148Communication
Link Range

0.00280.00280.00280.00280.0140RC Range

0.00280.00280.00280.00280.0140Magnetometer

0.00270.00270.00270.00270.0107

Max Airspeed

0.00190.00190.00190.00190.0074

Max Climb Rate

0.00130.00260.00130.00130.0066

Max Alt.

0.00160.00160.00160.00160.0062

Live stream

0.00150.00150.00150.00150.0058Beyond Line-OfSight Range

0.00140.00140.00140.00140.0056Frequencies

0.00120.00120.00120.00120.0048Low latency

0.00080.00080.00080.00080.0032

Encryption0.0003

0.00030.00030.00030.00030.0012

Final Score

0.25490.25770.27430.23451

18

Important note: The comparisons, numbers, and results presented in this paper, similar to any other
MCDA, are highly case-dependent. Although the initiated framework development can be applied to othercase studies, the presented results in this paper reflect the surveyed expert and the authors’ opinions.
Therefore, as long as the pair-wise comparisons meet the AHP’s consistency constraint, the developed
systematic framework in this paper can be adjusted to take into account the preferences of a new case study.
4 DEVELOPMENT OF COMPUTATIONAL FLUID DYNAMICS (CFD) MODELS OF UAVSFLYING NEAR, ALONGSIDE, AND WITHIN A BRIDGE
4.1 BackgroundA number of US states have performed research supporting deployment of UAVs to assist with bridge
inspections [36, 59, 60, 61, 62, 63]. Many of these studies have focused on establishing and enhancing thecapabilities of the UAVs to obtain the desired/required inspection data and support development of end
products such as inspection reports. However, other challenges exist that must be addressed before UAVs
can be moved from experimental technologies to standard use. Bridges are typically unique structures,
varying in construction details and terrain configuration, and exposed to a variety of weather conditions.
Issues such as wind gusts and wind-structure interaction can impact stand-off distances and the capabilityof a UAV to gather the data needed. Weather conditions and tight structural constraints can provide
challenging conditions for flight, and UAVs can be damaged or lost during inspection activities. A better
understanding of the fluid dynamics in the near-structure environment will help to optimize the flight anddata acquisition parameters required for successful use of UAVs to support bridge inspection. These
parameters include flight mode, standoff distance, flying speed, camera pointing angles, camera
parameters, and flight planning for weather conditions and future post-processing [64]. As a result, thegoal of the present work is to analyze the aerodynamic performance of a Skydio 2 UAV as a supplementary
tool for bridge girder inspections. The numerical prediction methodology, problem setup, as well as
assessment of parametric results are discussed in further detail.
4.2 Collection of Weather Data
To support development of computational fluid dynamics (CFD) models of UAVs flying near, alongside,
and within bridge structures, weather data was collected at a bridge on US 70 Eastbound (structure 4) overthe East End Connector in Durham, NC. The weather station was mounted in three positions, capturing data
above the deck, alongside the bridge, and at an interior position within the girders of the bridge (on adiaphragm). Weather data collected included temperature, humidity, wind speed, gust speed, and winddirection. In total, weather data was collected for approximately 4 months to support CFD modeling, withat least one month worth of data collected at each mounting position.

19

Figure 4.1: Location of wind speed anddirection sensors within interior diaphragmat bridge. Note mast containing dataacquisition/transmission equipment andtemperature/humidity sensors mounted near
exterior girder.

Figure 4.2: Location weather station mounted
above the deck.

Only the average wind speed and the gust wind speed were analyzed in this project. The data collected
above the bridge deck was compared to other four permanent weather stations located near the bridge.
The weather station data was accessed through the WeatherUnderground website [65]. Figure 4.3 shows
the average wind speed (a) and average gust (b) for the month of September. The Raleigh-DurhamInternational (RDU) airport station best agrees with the bridge data.

Figure 4.3 Wind speed (a) and wind gust (b) for the month of September 2020.
While the wind above the bridge can be estimated using an existing weather station. The under-bridge
environment is most relevant to the UAV. This environment wass impacted by (1) the shielding provided
by the bridge deck, (2) a channel effect provided by the roadway under the bridge and the end bent
structures, and (3) the bridge components such as girders, and diaphragms. Likewise, the gust data hasmore value when trying to predict the environment and forces encountered by a UAV inspecting the bridge

20

structure. Figure 4.4 shows the gust data for both the RDU weather station and the below bridge deck data.
Notice that the under-bridge data shows less variation in the wind speed (due to shielding) and lower
magnitudes.

Figure 4.4: Wind speed (a) and wind gust (b) during Hurricane Elsa, July 8, 2021.
The resolution of the weather station was not high enough to capture the gusts near the under-deck
bridge components. The impact of the resolution (2.5 mph) can be seen in the wind speed data as well. A
summary of the weather conditions measured at the bridge is shown during Table 4.1 below. The averagewind speed is below the resolution of the sensor and the average gust is near the resolution.
Table 4.1: Summary of weather station data.
Wind Speed (mph)Wind Gust (mph)Above Bridge
Sept. 4 – Oct 1, 2020

Max. 112
Avg. 2.6

Max. 22.5
Avg. 5.88

Below Bridge
May 10 – July 8, 2021

Max. 13.4
Avg. 0.3

Max. 26.8
Avg. 3.4

The limitations of the weather station sensors and the lower than expected wind gusts experienced
during the monitoring periods prevented the creation of transient boundary conditions for the CFD
analysis. The data general boundary conditions both above and below the bridge.
4.3 CFD Methodology and Numerical Setup for UAV Flight Simulation
The goal of the CFD modeling section of this work was to analyze the aerodynamic performance of theSkydio 2 UAV unmanned aerial vehicle (UAV) as a supplementary tool for bridge girder inspections. In
pursuit of this goal, this section of the present work presents aerodynamic performance evaluation results
via computational fluid dynamics (CFD) of the Skydio 2 UAV for bridge girder inspection. The numericalpredictions were performed using ANSYS FLUENT (v. 19). The CAD model was developed using
MATLAB and Solidworks CAD software. Images were taken of the UAV 1 2 Drone used in the present
work (shown in Figure 4.5) and were deployed to MATLAB software. MATLAB code was written totranslate pixels to scale measurement increments based on the true scale of reference dimensions of the
UAV. The computer aided design (CAD) model of the Skydio 2 UAV is shown below in Figure 4.6.

21

Figure 4.5: Photographs of the actual Skydio 2 UAV.

Figure 4.6: Developed Solidworks CAD model of the Skydio 2 UAV.
The CAD model was exported as a Parasolid file to ANSYS Design modeler where intricate parts ofthe model which would have a minimal effect on the fluid flow but would present issues for meshing were
removed. Faces were also healed in sections where there are sharp edges.
The ANSYS FLUENT standard k-ε model was used to model fluid flow. The standard k-ε is a semiempirical model based on model transport equations for the turbulence kinetic energy (κ) and its
dissipation rate (ε). The model transport equation for κ is derived from the exact equation, while the modeltransport equation for ε was obtained using physical reasoning and bears little resemblance to its
mathematically exact counterpart. The turbulence kinetic energy, κ, and its rate of dissipation, ε, areobtained from the following transport equations:22

In the equations above, Gκ represents the generation of turbulence kinetic energy due to the mean
velocity gradients calculated. Gb is the generation of turbulence kinetic energy due to buoyancy, and YM
represents the contribution of the fluctuating dilation in compressible turbulence to the overall dissipation
rate. C2 and C1ε are constants and σκ and σε are the turbulent Prandtl numbers for κ and ε respectively. The
Multiple Reference Frame Model (MRF) approach was also used to numerically predict flow around thepropellers of the UAV model. The MRF model is a steady-state approximation in which individual cellzones can be assigned different rotational and/or translational speeds. The flow in each moving cell zone
is solved using the moving reference frame. Further information on this model can be found in the ANSYSFLUENT User’s Guide.
4.4 Flow Domain
The meshed fluid flow domain is defined and illustrated in Figure 4.7 (a). The domain is split into a global
stationary domain and subdivided into four rotating domains. The rotating domains are defined by a small
cylinder enclosing the blades as shown in Figure 4.7 (b) (yellow meshed regions). The inlet, outlet, andsurrounding faces of the stationary region are dimensioned at a length of to accommodate the fulldevelopment of the upstream and downstream flow from affecting the results for the analysis. The inlet andoutlet boundaries are located both upstream and downstream from the centroid of the UAV body as shownin Figure 4.7 (c).
The mesh was generated using ANSYS meshing software. The quality of the computational meshdirectly influences the results in terms of the rate of convergence and grid independence. For the present
work, the cell sizes of the mesh were generated using a face sizing and tetrahedral meshing scheme
unstructured in both stationary and dynamic (rotating) regimes. The selection is based on therationalizations that unstructured tetrahedral grids have the capabilities to discretize complex geometries
with fast and minimum user intervention. For grid independence, the first mesh was generated with aminimum face size of 0.004m and decreased in length scale increments of 20% until the delta change interms of comparing current to previous mesh parametric results (analyzing total pressure, wall shear, andvorticity) were minimized to less than five percent error. Table 4.1 provides details for the final grid
generation at which grid independence was achieved.

23

Figure 4.7. Fluid flow domain: (a) meshed drone body wall (b) UAV wall and rotating domains, and (c)global stationary region enclosed around the UAV and rotating domains.

24

Figure 4.8 shows the coarse mesh version of the surface and volumetric mesh of the propeller blade androtating domain. A much finer mesh was used for running the simulations after grid independence wasachieved.

Figure 4.8: Coarse mesh of rotating domain enclosing the propeller.
The boundary conditions imposed on the model are summarized in Figure 4.7. At the inlet boundary,
the free-stream velocity averaged around 1 m/s with a turbulence intensity of 0.1%. The turbulence
intensity was set based on the wind tunnel intensity measured in references [66, 67]. A pressure outlet
boundary condition was set at the flow exit downstream of the flow domain as shown in Figure 4.7. A noslip condition is set on all the walls (remaining conduit wall faces, drone body faces, and propeller faces).
The domain that enclosed the propeller blade was assigned with the MRF to incorporate the rotational
speed. The method is highly fitting for the analysis, which requires the interaction between stationary androtating regimes. The individual zones were assigned with discrete rotational or translational speed. Theinterface needed between the two zones undergo a local frame transformation to enable the flow variablefrom one zone to be used by the adjacent zones. The moving reference frame was assigned with a rotational
speed of 6000 RPM. The wall forming the propeller blade and the hub were also assigned as rotational,
with a velocity of zero with respect to the adjacent cell zone. The pressure–velocity coupling was selectedusing a Semi-Implicit Method for Pressure-Linked Equations (SIMPLE). The Second Order Upwind
scheme was used for momentum and pressure. The First Order Upwind for both Turbulent Kinetic Energy

25and Turbulent Dissipation Rate and the Least Square Cell-based Algorithm were used for the gradients.
The time step sizes used for transient analysis was set to 0.00015s.
For all CFD simulation analyses, the force and momentum were resolved in a three-coordinate system(x, y, and z). The axial force and momentum around the propeller axis define the thrust and torque,
respectively. The total pressure is also calculated and defined as:

Figure 4.9 provides a plot of the velocity vector and contour plots. As mentioned previously, thevelocity of air flow from the inlet of the global stationary domain was 1 m/s. From Figure 4.9 (a)., thehighest velocity magnitudes of flow occur around the tips of the rotating propellers (16.1 m/s). Thissuggested the flow is increased by high levels of thrust and lift forces produced by the torque output ofthe propellers. This is evident as shown in the contour plot in Figure 4.9 (b).

Figure 4.9: Velocity plots: (a) vector plots around rotating domains and (b) top view of UAV and velocity
contours of rotating domains.
The red regions suggest that high levels of torque were produced that forces the flow outward of therotating domains (high velocity regions highlighted in red). Figure 4.10 shows the total pressure contours
around the drone body and rotating domains at 0.015s. The total pressure ranges from -61.8 Pa to 46.8 Pa.
The pressure was negative because the pressure gradient drived the flow from high pressure regions to
low pressure regions. Also, in the case of regions where there is flow separation, the low pressure insidethat region will be relative to the lowest fixed pressure and can result in negative pressure. For the26

propellers, the pressure difference between the front and back sides of the propellers createed a force inthe forward direction resulting in thrust which made the UAV fly. In the case of the UAV body the pressure
was near constant value around the body of the drone and showed higher pressure values near the tip of
the propeller blades.

Figure 4.10: Total Pressure at time 0.0015s

Figure 4.11: Plot of propeller performance parameters.

A better representation of the performance of the UAV can be found by analyzing the moment
coefficient (torque), drag coefficient (Cd) coefficient (Cm), lift coefficient (Cl), and thrust force
magnitude. Figure 4.11 presents a plot of these parametric results. All of these parameters wereaveraged over all four of the propellers of the Skydio 2 UAV. As shown in Figure 4.9., the27

maximum thrust averaged around 0.035N. The maximum Cd, Cl, Cm, and Cd, averaged around 0.00013, -0.00292, -0.03312, and -0.00202. The values of these coefficients and force magnitude
show indication that the UAV is suitable for flying in the given conditions. However, more
simulation data is needed such as varying inlet boundary condition velocity magnitudes to show
flight conditions in windy scenarios that would make bridge inspections difficult if not
impossible.
4.5 CFD Methodology and Numerical Setup for Near-field Bridge Environment
A computational model of the bridge on US 70 Eastbound (structure 4) over the East End Connector in
Durham, NC bridge was created in SolidWorks, Figure 4.12. (a) shows the entire bridge model. Figure
4.12. (b) shows the view of the understructure including the girders, diaphragms, and center support.
Figure 4.12. (c) shows the end bent and Figure 4.12. (d) shows the pot bearing.

Figure 4.12: Solid model of structure 4.
To create a model that could be used in the CFD analysis the following simplifications were applied:●●●●

The bolts, welds, and fasteners were not included.
The girders were treated as continuous, i.e. the splices were ignored.
Concrete joints were ignored. This applied to the bridge deck and concrete barriers.
The corrugated metal under the bridge deck was not included.

The area between two girders with a series of three diaphragms was analyzed. The girder surfaces andthe underside of the bridge deck are treated as solid walls. A constant velocity inlet condition was
specified, and air exited through the end of the channel and the lower face. The entrance region from theinlet to the first diaphragm is 8 ft. The space between the diaphragms is 13 ft. which is the distance near
the end bents of the actual bridge. This spacing provided the most cumulative effect. The diaphragm ended
20 inches above the bottom surface of the computational domain.
The computational grid consisted of 3,728,857 elements. A transient, turbulent calculation was

28

performed with a timestep of 0.01 seconds. The inlet velocity was 2 m/s or 4.5 mph. This velocity wasslightly higher than the average gust velocity but less than the maximum values measured by the weather
station when positioned under the bridge deck. The presented results are shown at a simulation time of 4
seconds.
Figure 4.13. shows the velocity contours through the series of diaphragms. Planes perpendicular to thediaphragms were used to display the fluid behavior. The first plane (left) is at the center point and thesecond plane (right) centered between the middle and wall. The second position is referred to as the quarter
plane. This center position gives the best view of the jet that was formed through the large opening andthe shielding provided by the gusset plate. The second plane best shows the flow disruption by theelements in the diaphragms.

Figure 4.13: Velocity contours through a series of diaphragms along the center (left image) and thequarter plane (right image) with the bridge geometry.
The velocity along the long planes is presented below. The center plane shows the higher velocity jetthat forms through the opening above the gusset plate. Notice that the jet becomes less defined in thesecond and third diaphragms. This is because the flow encountering the diaphragm is not a uniform, fully
developed flow pattern. The shielded region below the jet also becomes less defined. The quarter planshows less defined flow patterns.

Figure 4.14 Velocity contours through a series of diaphragms along the center (left image) and the
maximum velocity (right image).

29

The turbulence kinetic energy is shown on the two planes, Figure 4.15. The highest turbulence energy
can be seen on the quarter plane in the region where the multiple fluid streams are remixing behind thediaphragm.

Figure 4.15 Turbulence kinetic energy contours through a series of diaphragms along the center (leftimage) and the maximum velocity (right image).
Views perpendicular to the plane above are also shown. Below are the velocity contours on planes 6
inched behind each of the three diaphragms. The inlet is to the right in the image. The effects of thediaphragm elements in the flow patterns are clearly seen in Figure 4.16.

Figure 4.16: Velocity contours through a series of parallel to the diaphragms.
A series of the positions behind the first diaphragm are compared. The first was located 6 inches behindthe diaphragm, the second at the mid-point, 6.75 ft, and third is 6 inches before the second diaphragm. By
the final image the flow is becoming more uniform with the impact of the wall friction still evident.

30

Figure 4.17: Velocity contours with increasing distance from the diaphragm.

4.6 Summary of findings
The parametric results for the Skydio CFD studies show reliable moment coefficient and thrust making
the Skydio 2 UAV suitable for bridge inspection in low wind (1 m/s) conditions. A second simulation was
run on a model of the bridge on US 70 Eastbound (structure 4) over the East End Connector in Durham,
NC bridge with a varying inlet velocity. Data from this simulation is essential to show flight conditions inwindy scenarios that would make bridge inspections difficult if not impossible. The results presented fromthe bridge model studies show high levels of unsteadiness throughout the diaphragm and areas low to theground. These are regions where the highest levels of fluid energy losses and high circulation would occur
and make flight difficult during high wind scenarios (wind speeds greater than 1 m/s). It appears that thehighest flow recirculation zones occur near the entrance region and dampen with distance. The velocity
magnitude decreases, but recirculation patterns continue as a cause of acceleration and deceleration of air
due to cross-sectional changes and variations in the flow conduit (area under the bridge). These are theregions where flight would be difficult due to flow recirculation and wakes. Future work is needed where
the UAV and the bridge is modeled together to show the level of fluid forces acting on the UAV as afunction of inlet velocity conditions.

5 DESIGNING EXPERIMENTS TO TEST AND EVALUATE THE PERFORMANCE OF THE
UAVS FOR BRIDGE INSPECTION

31

5.1 Introduction
The first part of this chapter discusses UAV features the research team deemed important for bridge
inspections. The various criteria under each category were reviewed, with respect to how each criterion
can affect bridge inspection tasks. Some of these features are provided in the manufacturers specification
document and thus were retrieved from manufacturer’s published literature. In addition, the research teamdesigned different experiments for the evaluation of UAV capabilities that are important for bridge
inspection. In this section, different experiments will be presented for testing various capabilities of UAVs.
5.2 Experiment Design Process
The test and evaluation experiment design process follows a well-established scientific method. It is asystematic and straight forward approach that includes the problem definition, objective establishment,
articles of experiment selection, experiment execution, verification of results consistency and conclusion.

Figure 5.1: Experiment design process.
The problem definition stage set out clearly the desired goal of the experiment. This stage also involves
identifying all response variables for the device under test (DUT). In the current work, the responsible
variables were identified using a combination of literature and advice from experts in infrastructure
inspection. The response variables deemed critical for bridge inspection are flight performance, situationalawareness, communication and payload. Each of these variables/categories have sub-variables or criteria
as indicated in Table 4.1 These sub variables are the key process input variables that will be tested andevaluated. The second stage of the experiment design involves the establishment of the objective. This
design stage involves outlining what is expected to be achieved with these experiments. In this work, someof the objectives aim to verify whether or not the DUT adheres to the specification information indicatedin the specification documents. In other cases, the objective aimed to examine the response of the DUT
under both normal and abnormal conditions. Following the objective definition, the articles of theexperiments were identified. The article varied depending on the experiment but in all cases the UAV waspart. For experiments that involved generating radio frequency (RF) signals, all regulatory rules regarding
RF signal use to ensure other services are not impacted were adhered to. The final step involved
experimental execution, data collection and results analysis. In most cases, subjective means to quantify
the results of our analysis were used. However, where possible, quantitative analysis was performed.
5.3 RF Immunity Testing
UAVs are remotely piloted with the command-and-control signals, and the telemetry data as well the
payload data is transmitted via RF channels [68, 69, 70]. The frequencies used by the command-and-control
links of UAVs are typically in 72-73, 902-928 and 2400-2483.5 MHz bands [71], while the video
transmissions are usually made via 5.2 GHz bands. These frequencies are not used solely by the UAVs.

32

There is a proliferation of services that use communication networks such as, WiFi, ZigBee, LTE, and GSM
that operate on the same frequency bands. Therefore, there is a high chance for these services to impact theperformance of UAVs particularly in environment where there is a proliferation of RF sources [72].
Federal Communication Commission (FCC) requires all electronic equipment including UAVs to meeta minimum Electromagnetic Compatibility (EMC). UAVs are usually manufactured for general-purpose
use which is always governed by a trade-off between the performance and cost-effectiveness, and therefore,
it is unlikely that the manufacturers will be able to handle all environmental conditions and maintain the
EMC for all possible RF scenarios that a UAV may encounter during its operation.
Thus, in this section, the UAV’s immunity to interference is investigated by designing and conducting
the field experiments that can subject the UAVs to controlled electromagnetic interference and distortions
that are usually encountered in a UAV-enabled bridge inspection environment. To achieve this goal, thesubject UAV platforms were exposed to different levels of interference. It is followed by an analysis of
how the presence of interference impacts the UAV’s response to the controller’s control commands. Finally,
the impact of the RF stress on the transmission of telemetry data and payload (i.e., camera) data from the
UAV to the controller is 30investigated. Here, only the RF immunity experiment is described. In chapter 4,
the results are presented.
5.3.1

RF Immunity test setup

The main test articles for the RF immunity test are UAV’s, Agilent N9310A RF signal generator and Agilent
CXA 9000 signal analyzer. The experiment was conducted using three general-purpose UAVs. We
compared the results of the three UAVs, UAV 1, UAV 2, UAV 3, UAV 4 and to determine the one withthe strongest immunity to RF interference. The electromagnetic interference was created using Agilent
N9310A signal generator. It supports frequencies in the range of 9kHz – 3GHz, and thus, its range covers
the operating frequency for the UAV’s communication channels as well as other RF signals that are likely
to interfere with the UAV’s channels. Furthermore, we used the Agilent CXA 9000 signal analyzer tomeasure the environmental average RF signal power levels, the UAV’s Remote Command (RC)transmission power, and the interference generated by Agilent N9310A signal generator. It supported
frequencies over the ranges of 700MHz - 3 GHz. The signal generator and analyzer were connected to awide-band antenna that supports their respective frequencies. Figure 5.2. shows the setup used for
conducting the tests. Since this experiment involved RF interference generation, it was carried out inaccordance with the Federal Aviation Authority (FAA) and the Federal Communication Commission (FCC)
regulations. The tests were conducted away from commercial radio, TV antennas, and FAA or militaryradars to ensure that the tests did not impact their services.

33

Figure 5.2: RF test setup.
5.3.2

Test procedure

As an initial step, the frequency range of 2 to3GHz was scanned to establish the environment’s RF signal
power using RF Signal Analyzer. The UAV and controller/controller were then turned on. The UAV wasthen positioned initially at approximately 1m from the signal generator. At this point, no interference wasobserved. The status of the RF and video feed was checked, in addition to all other information discussed
above, to ensure that the UAV is operating as expected. The UAV was then moving away from theinterference source at discrete steps. The impact of the interference on GPS signal, video feed, commandand control signal was then observed.

Figure 5.3: Pictorial view of RF experiment
5.4 Endurance Testing34

Endurance testing provided an indication of the maximum flight time that can be achieved with a fully
charged battery. The testing of this operational criterion is important because bridge inspection can takehours, days, or weeks (for long bridges such as the Virginia Dare Bridge) to complete depending on thenature of inspection i.e., in-depth inspection or routine inspection. While a UAV with limited flight time
may suffice in inspection tasks such as in-depth inspection of specific superstructure and substructureelements or appurtenances, it may not be appropriate for bridge deck inspection. Therefore, it is importantto evaluate the endurance to identify the most appropriate platform for a particular situation. Although thebattery time is usually listed in the manufacturer’s specification document, that value indicates hoveringmode endurance. In bridge inspection, usually the UAVs will be used in forward mode, thus this test isimportant to determine the true battery life of the respective UAVs.
5.5 Endurance test setup
The test setup of endurance included all the three UAVs mentioned in the previous section. A stopwatch
was used to keep track of how long the UAVs battery last. For all the UAVs, the amount of time it took forthe battery to drop 25 % of its capacity was recorded. For UAVs with auto-land feature, the research teamalso recorded the time it took to initiate auto-landing. The experiment was executed in both hovering andforward mode.
5.6 Hovering mode endurance test procedure
1.
2.
3.
4.
5.
6.
7.
8.

Equip the UAS platform with fully charged batteries.
Take off from a suitable location and hover the UAV at a certain altitude.
Fly the UAS in hovering mode until reaching 20\% of the battery level.
Record the flight time.
Continuing flying until low-battery alarm is activated or auto-landing initiated.
Record the flight time.
Land the UAS.
Repeat the above process for 3 times and take the average flight time in both cases.

5.7 Forward mode endurance test procedure
1. Equip the UAS platform with fully charged batteries.
2. Plan and load a mission onto the UAS memory device (applicable to UAS that support thisfeature).
3. Continuing a fixed linear path flying until low-battery alarm is activated or auto-landing initiated.
The same path was used for all UAVs.
4. Record the flight time.
5. Land the UAS.
6. Repeat the above process for 3 times and take the average flight time in both cases.
5.8 Vibration Testing
Rotor and gimbal vibration can introduce noise to images captured with UAVs [73, 74] and may
subsequently affect the effectiveness of algorithms used for post-processing of the images. Depending on
the quality of the UAVs gimbal’s controller, the vibration impact will be different. Therefore, our objective
was to design an experiment to test the UAS’s capability of handling vibration effects on image quality.
This was achieved by comparing successive images taken in a short time with the UAV in hovering mode.
he peak signal to noise ratio (PSNR) of each image against a reference image was computed using theequations below.

35

𝑃𝑆𝑁𝑅 = 10𝑙𝑜𝑔
𝑟𝑜𝑤−1 𝑐𝑜𝑙−1

𝑀𝑆𝐸 =

𝑀𝐴𝑋𝑖
√𝑀𝑆𝐸

1
∑ ∑ ||𝐼𝑚𝑎𝑔𝑒𝑟𝑒𝑓 (𝑖, 𝑗) − 𝐼𝑚𝑎𝑔𝑒𝑡𝑒𝑠𝑡 (𝑖, 𝑗) ||𝑟𝑜𝑤 ∗ 𝑐𝑜𝑙𝑖=0

𝑗=0Here, the first captured image is used as the reference image. In an ideal situation where vibration has
no impact on the image quality, it could be expected that the PSNR would be zero since all the images willbe the same.
For an example, consider a 25-pixel reference and test image shown in Figure 5.4. Here, row=5 andcol=5. MSE is computed by first finding the difference between corresponding pixel values in bothreference and test images. The norm of the difference is computed and then divided by the resolution of theimage. 𝑀𝐴𝑋𝑖 depends on the number of bits used to represent each pixel. If 32 bits are used 𝑀𝐴𝑋𝑖 = 232−1

Figure 5.4: A 25-pixel image.

5.8.1

Vibration Test Setup

The test articles for vibration testing included the 3 UAVs platform and a test element. The different UAVs
were used to capture successive images of the test element. The first image was considered to be thereference image and subsequent images as test images.
5.8.2
1.
2.
3.
4.
5.
6.

Vibration Test Procedure
Attach a test element with a known and visible defect onto a section of the bridge’s upper deck.
Take-off and fly to a suitable altitude
Set the UAV into hovering mode if it has this functionality.
Take two images of the test elements at intervals of about 1 seconds.
Repeat the previous step an arbitrary number of times.
Compute the PSNR between the first captured image and the second image using the equation
above.

5.9 Global Position System (GPS) TestingGPS signals around bridge infrastructure are usually unstable, and as such, there is a likelihood of GPS
signal loss during inspections especially at the underside of the bridge deck. This can result in UAV crashes

36especially when operating in GPS mode [75]. It is also known that electromagnetic interference can impact
the reliability of the GPS signals. Therefore, to ensure an accident-free bridge inspection, there is a need toevaluate the reliability of the GPS of the platform which is selected for bridge inspection to provide a safe
performance. In this work, the time it takes for the GPS to recover when there is an abrupt loss in GPS
signal was evaluated. This is called the Time-to-recover.
5.9.1

GPS test setup

The test articles for GPS testing comprise of the 3 UAV platforms, a stopwatch and an aluminum metalshield. Aluminum is known to obstruct GPS signals thus we used it to simulate GPS denied environments
likely to be encountered by the UAVs when operating under the bridge deck. The test was set up in open
space where the GPS signal is known to be very good. Figure 5.5. shows the initial setup of the test. At thispoint we have allowed the UAV to establish full GPS connection. The final stage of the test, where the
UAV was covered with the aluminum shield to block the GPS signal is also shown in Figure 5.6.

Figure 5.5: GPS test setup: final state.

Figure 5.6:: GPS test setup: initial state.
5.9.2

GPS test procedure

1. Turn on the platform and wait until the GPS status changes from “searching” to “OK” or GPS
signal passes the initial oscillations and reaches a stable situation.
2. Cover the UAS with a metal shield for about two minutes, to deny the GPS signal.
3. Remove the metal shield and observe the time it takes for the GPS status to change from“searching” to “OK” GPS signal passes the initial oscillations and reaches a stable situation.
4. Record the time using the stopwatch.
5. Repeat the above procedure for 10 times and then find the average.
5.10

Illumination Testing

The level of illumination under bridges can greatly impact the quality of images taken with the camera
mounted on the UAV. The illumination level becomes crucial when inspecting components of the bridge
under the deck. Aside from the natural illumination provided by the sun, most UAVs are equipped with
LEDs to provide some form of illumination during the inspection. This can be checked by visual inspection.
However, it is important to evaluate the performance of the payload camera when used to capture images
at different times of the day. This test was conducted inside a room with variable lightning control. A sample
test piece with the UAV was examined under different lighting conditions, to observe how the reduced
lighting impacted the ability of the UAV to detect defects in the test piece.

37

5.10.1 Illumination test setup
The main articles for the illumination test included 2 UAVs, a mobile phone with light-meter installed, testelement (with known defects) and a room with variable lightning control. The test element used was aconcrete block with some defects. Figure 5.7 shows the test element on a stand inside the test room. The
background of the test room was chosen to provide good contrast in color. The different illumination levels
were estimated using the light-meter. Although the light-meter may not be the most accurate illumination
measurement tool, it has been shown to provide reasonable values within the lux range used for conducting
this experiment [76]. Four different lightning conditions were considered to simulate the illumination
scenario that is likely to be encountered during bridge inspection. These are:●●●●Level 1 (Dark): 10 - 130 lx
Level 2 (Intermediate 1): 130 - 220 lx
Level 3 (Intermediate 2): 220 - 250 lx
Level 4 (Bright): above 250 lx

Figure 5.7. Illumination test setup.
5.10.2 Image Quality Assessment
Although we can visually determine which UAV camera performs best, we computed the entropy of each
image using the equation below.
𝑛𝐻(𝑥) = ∑ 𝑝𝑖 𝑙𝑜𝑔2 𝑝𝑖
𝑖=138Here, 𝑝𝑖 value is the occurrence probability of a given symbol. For images, the symbols are the pixels.
Entropy is a statistical measure that gives an indication of the average information content in an image. It
depicts the information content in the aggregated features of the grayscale image. The research team’s
assumption was that if the image has a high quality and sharp enough to enable detection of defects, thenits entropy should be higher. This metric makes comparison between the images captured by the different
UAVs straightforward and eliminates human bias in interpretation.
5.10.3 Illumination test procedure
1.
2.
3.
4.
5.
6.
7.
5.11

Turn on the UAV and camera.
Initially, set the illumination in the test room to level 1.
Position the test element at 60 cm from the UAV.
Inspected and captured images of the test element with UAV's camera.
Repeat steps 3 and 4 using lightning levels 2 to 4.
Repeat the above for all three UAVs.
Repeat the above using 120 cm for step 3.
Autonomy Testing

The autonomy testing evaluated the capability of the UAVs to execute planned missions as well as thecapability of execution of safety features for managing emergency situations. The research team considered
UAVs that have support for autonomous waypoint following, auto landing, and return to home functions.
These autonomous functionalities can be crucial for safe bridge inspections. For instance, the UAVs can be
programmed to return to home, when there is a communication link loss between the UAVs and thecontroller to avoid crashing. In addition, the inspection of the bridge facade and upper deck can be achieved
by loading a planned mission on the UAVs for autonomous inspection. This reduces the human factors’
influence in the inspection process, and thus reduces accidents and improves efficiency. In the following
section, the tests for waypoint following, return-to-home, and lost-link recovery are described.
5.11.1 Autonomy Test Setup
The main articles for these experiments are the UAV platform. This test was carried out on a large football
field. A sample mission that took approximately 10 minutes was planned, using third-party software. Threedifferent functionalities were examined under autonomy. These are autonomous waypoint following, auto
landing, and return to home functions. Before flying, "return to home" was activated for UAVs that support
this feature.
5.11.2 Autonomous waypoint-following procedure
1. Load a planned mission onto the UAV's memory device (applicable to UAVs that support thisfeature).
2. Take off and allow the UAS to fly autonomously following the waypoints of the loaded mission.
3. When the mission is complete, land the UAV.
5.11.3 Auto-land procedure
1. Take off and fly the UAV in a suitable area.
2. Continue flying following mission's waypoints until auto-landing is activated (for supported
UAVs)
3. Allow the UAV to land itself.

39

5.11.4 Return to Home Test Procedure
1. Take off and fly the UAV in a wide open area.
2. In the middle of the mission, turn off the controller/RC to enable the lost-link recovery process if
it is safe to test.
5.12

Maneuverability Testing

The inspection of certain bridge components may require the UAV to navigate through confined spaces.
For instance, the inspection of the steel/concrete box girders, steel arch rings, arch tie girders and somebearing types, etc. may require the UAS to perform a range of maneuvers in confined spaces. Furthermore,
the inspection of bridges of many types (such as steel truss) or configurations will typically require flying
the UAS between the members in order to reach certain components of the bridges. Thus, there is a need totest the maneuverability of candidate UAS platforms to identify platforms that can perform well in thebridge inspection scenarios.
5.12.1 Maneuverability Test Setup
The scope of this test was limited to testing and evaluating confined space navigation and agility. The main
articles for the experiment were 3 UAV platforms. The exit criteria for this experiment is successfulnavigation in confined bridge areas. The main idea of this test is to examine how easily the candidate UAVs
can navigate around obstacles to carry out inspection.
5.12.2 Maneuverability Test Procedure
1.
2.
3.
4.
5.13

Select a suitable bridge for testing.
Take-off and fly towards the bridge.
Navigate through the concrete piers while inspecting the underside of the bridge deck.
Observe the ease with which each UAV and its capability to navigate its way through the piers.
Agility Testing

Agility is another criterion that is worthy of testing due to its importance to bridge inspection. It gives anindication of how quickly a UAV's flight path can be altered when commanded to do so. In other words, itis the measure of the maximum possible rate of change of the acceleration vector at any point in the flightenvelope [77]. The higher agility, the better the UAS can withstand turbulence and gusts. This becomes
particularly important when the UAS is used for close-range inspections in confined areas likely to be
encountered in bridge inspection. Therefore, in this experiment, the UAV's time constant in different
channels was measured as an index for the response time and agility of the platform. The time constant, τ,
is characterizing the response time to a step input. A UAV with a shorter time constant responds quickly to
input change whereas, a UAV with a long time constant behaves sluggishly. To be able to generate aconstant input into the input channels, we require the RC control rods to push to the maximum, and hence,a large test field is needed to allow execution of this test.
5.13.1 Agility Test setup
The scope of this test was limited to the response of the UAV to issue commands from the controller. The
main articles for the experiment are the 3 UAV platforms. The exit criteria for this experiment is successfulnavigation in confined bridge areas. The main idea of this test is to examine how easily the UAV cannavigate around obstacles to carry out inspection. The UAV time constant in different channels was used
as an index of its agility. The time constant, τ, is characterizing the response time to a step input.

40

5.13.2 Agility Test Procedure
1. Identify a suitable location that adheres to the test envelope.
2. Take-off and fly to a suitable altitude and hover the UAVs.
3. Apply a constant input (if possible maximum input) to Aileron, Elevator, Throttle, and Rudder onboth directions until reaching a constant speed.
4. Log the flight data using the UAS on-board system or custom IMU-GPS system.
5. Find τ as the time when the UAS’s speed reaches 0.63 of its maximum speed. In this study, theresearch team determined the different time constants for each of the channels and each of thedirections.
5.14

Wind Gusts Tolerance Testing

The performance of UAVs (and in particular the quadrotors) is well studied in the low advance-ratio flight
conditions but not well enough in largely disturbed flow conditions such as wind gusts. The real-timemeasurements of flow conditions around the surface of the quadrotor are required for understanding theflight condition and the influence of local wind. The key difference between wind gusts and turbulence tests
are the duration and point of applied pressure. While the wind gusts tests aim to achieve impulse pressure
differentials on a small area of the UAVs fuselage, the turbulence tests aim to achieve constant pressure
differentials on the UAVs.
5.14.1 Wind Gusts Test Setup
The scope of this test is limited to studying the UAV platform’s stability against wind gusts conditions. The
test articles include the UAVs, an electric fan capable of producing wind flows ranging from 3 m/s to 5 m/s
and the custom IMU-GPS flow sensor. Figure 5.8 shows the experimental setup with the UAV marked withthe red box.

Figure 5.8. Setup for Wind Gust Experiment.
5.14.2 Wind Gust Test Device Development

41

A customized inertial and flow measurement board was constructed to capture the required measurement
needed to study the behavior of the different UAVs in wind gusts. The flow measurement board consistedof a microcontroller, GPS, inertial measurement unit (IMU) and differential pressure sensors. Figure 5.9
shows the block diagram of the custom hardware. The components on the board include a microcontroller,
IMU, GPS, pressure sensors and an external battery.

Figure 5.9: Block Diagram of Custom Inertial-Flow Sensor Hardware.
Figure 5.10 shows the developed testing and measurement device which is mounted on one of the UAVs.
The components of this device were carefully selected to be of small size and weight to minimize the impact
on the UAV’s flight dynamic.

Figure 5.10: Custom flight data collection mounted on a UAV.
5.14.3 Wind Gusts Test Procedure421. Mount the designed pressure sensor on the top of the studied platform to measure the pressures infour directions. To reduce the effect of rotors’ turbulence on the sensor measurements, sensor
probes should be extended by tubes. The size and length of tubes should be chosen based on thedimension of the UAVs and its motors.
2. Apply the external disturbance using an electric fan with a varying wind speed to generate thepressure differences. The disturbances (pressure jets) will be applied horizontally which isinstalled on a structure with about 2m height.
3. Move the UAVs platforms on a straight line, perpendicular to the direction of the fan flow.
4. Measure the wind gust using the mounted pressure sensors.
5. The maximum acceleration experienced by the UAV as it flies and passes the fan can be obtained
from the IMU logs. The impact of the wind gust on the UAVs stability is quantified in a defined
metric Impact of Acceleration Change, which is computed using the following equation:
𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛
𝐷𝑃. 𝐴
Here, DP is the differential pressure that causes the maximum acceleration change and A is the surface
area of the UAV the pressure is exerted.
𝐼𝑚𝑝𝑎𝑐𝑡 𝑜𝑛 𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝐶ℎ𝑎𝑛𝑔𝑒 =6 TESTING AND EVALUATION RESULTS FOR THE CANDIDATE UAVS
6.1 Introduction
Using the experiments designed and described in Section 5, four commercially available UAVs having
different specifications were tested to evaluate the performance of each UAV in each category. The four
UAVs used are identified as UAV 1, UAV 2, UAV 3 and UAV 4 to avoid commercial advertisement. In
the sections that follow, the results of each of the platform in each performance test is described, along witha comparison of how the UAVs. Special attention was paid to the implications of each UAV’s performanceon its potential use for bridge inspection. It is important to note that some of the platforms were notsubjected to testing under some categories because the commercially available UAV did not support suchfunctionality, or because the UAV was not available at the time of the test. In particular, UAV4 crashedfairly early in the testing program, and wasn’t available for majority of the tests.
6.1.1

RF Immunity

At the onset of the RF immunity test, the test area was scanned to establish the baseline environmental RF
noise in the 2 GHz frequency band. This is because each UAV tested transmits its command-and-control
signal using this band. Figure 6.1 (a) depicts the control band together with the environmental noise on oneof the UAVs. We observed a 5dB power level increase above the environmental noise in the bandwidth of
2.395- 2.481 GHz, when communication between the UAV and controller was established. The noise inthis plot is the natural environmental noise observed prior to the introduction of artificially generated noise.
Similar results were obtained for all other UAVs since they operated in the same frequency range.
6.1.2

RF Interference impacts on command signal, GPS, Video-feed

As indicated in RF immunity test description, the UAVs under test were exposed to RF interference using
two methods. The first method involved the generation of a random 15 dBm continuous wave RF signal
within the UAV's operating frequency range. The second method involved a 20dBm sweeping frequency
within the UAV's operating frequency range. The RF noise together with environmental noises of UAV 1's
control signal are shown in Figures 6.1, 6.2, and 6.3.

43

Figure 6.1 (a) : UAV control channel and environmental noise.

Figure 6.2 (b) : Sustained RF noise

Figure 6.3 (c): Sweeping RF noise.
After the introduction of the RF noise using the two methods described above, the control commands,
GPS, and video link status for UAV 1 were checked. It was observed that none of the functionalities of

44

UAV 1 were impacted for both the random 15 dBm sustained RF noise and 20 dBm sweeping noise. We
repeated the above for two of the other UAVs since UAV4 had crashed and was unavailable at the time ofthis experiment. The results for all UAVs and test parameters using both interference generation methods
are summarized in Table 6.1. and 6.1. It is evident from Table 6.1 that the connection between UAV 3 andits controller is disrupted when exposed to RF noise. The other UAVs seem to have greater RF immunity
than UAV 3. The reason for the immunity differences is explained in the next section.
Furthermore, it was observed that most of the functionality of UAV 3 were impacted even at lower noise
power levels than were used for the UAV 1 and UAV 2. The RF noise power above -23 dBm severely
impacted the operation of UAV 3. In most cases, GPS, video, and RC signals are impacted as shown in
Table 6.2. Also, when UAV 3 was subjected to a sweeping RF noise of 10 dBm in the band of its operation,
we observed a reduced quality of the video feed even though the RF was working properly. This is shownin Figures 6.4 and 6.5.
Table 6.1: Sustained Noise RF immunity test results.

UAV

RF Noise

Distance tocontroller RC Impacts
(m)

Video
Impacts

GPS
Impacts

15 dBm power

1

None

None

None

15 dBm power

5

None

None

None

15 dBm power

10

None

None

None

15 dBm power

20

None

None

None

15 dBm power

1

None

None

None

15 dBm power

5

None

None

None

15 dBm power

10

None

None

None

15 dBm power

20

None

None

None

-50 dBm power

5

None

None

None

UAV 2 -30 dBm power

5

None

None

None

-17 dBm power5

Not OK

Not OK

Not OK

UAV 1

UAV 3

Table 6.2: Sweeping Noise RF immunity test results.

UAV

RF Noise

Distance tocontroller(m)RC
Impacts

Video
Impacts

GPS
Impacts

15 dBm power

1

None

None

None

15 dBm power

5

None

None

None

UAV 1

45

15 dBm power

10

None

None

None

15 dBm power

20

None

None

None

15 dBm power

1

None

None

None

15 dBm power

5

None

None

None

15 dBm power

10

None

None

None

15 dBm power

20

None

None

None

UAV 2 -17 dBm power

5

None

None

None

UAV 3

Figure 6.4: Image quality of UAV 3 for RF noise <−17dBm.

Figure 6.5: Image quality of UAV 3 for RF noise =17dBm.

46

Figure 6.6: RF Channel switching between default and alternate band.
6.1.3

RF Channel switching between default and redundant channel.

It was observed that, although all UAV have inbuilt mechanism to switch to a different frequency band
when the default one is impacted by a RF noise, UAV 3 takes a longer time to make the switch. In Figure
6.7, it can be observed that when an interference was introduced to the UAV 2 's default frequency (2.444
GHz), the UAV automatically switched to a different frequency, i.e., 2.461 GHz. It took the UAV
approximately 15 seconds to make the switch to the new frequency and during that period all thecommunications were lost.

Figure 6.7: Image quality of UAV 3 after exposure to RF noise>−17dBm.
6.2 Endurance
The endurance of candidate UAVs is key to enhancing the efficiency of UAV-enabled bridge inspection.
Here, the results for the endurance tests are presented for all four UAVs. The specifications document
indicates the battery life of the UAV; however, this is usually measured in hovering mode. Since bridge
inspection usually involves flying the UAV in the forward mode, it was imperative to test the UAV in thisforward mode instead. In addition, the UAVs were also subjected to hovering mode testing to confirm the47

endurance values indicated in the specification documents.
6.2.1

Forward mode endurance testing

Performance results in the forward mode endurance testing are very crucial to bridge inspection because
this test gives an accurate depiction of the UAV's effective usage time. Unfortunately, none of the existing
general-purpose UAV platforms report data on forward mode endurance in their specification manual. The
endurance information provided in all the UAVs specifications manual are all based on hover mode testing.
Thus, in this section, results are presented for forward mode endurance tests. In executing this test, the time
it takes for the UAVs battery level to drop to 25% was measured. The time it took for the battery to reachcritical levels causing the UAV to execute auto-landing for platforms that supported this feature was also
determined. Additionally, the time it took for the UAV to sound an acoustic warning to the pilot to land
was measured. This test was conducted outdoors and thus, an air meter was used to measure the surrounding
wind speed. This was to ensure that the tests were performed in near similar conditions, enablingcomparison of the results. Table 6.3. summarizes the results of forward mode endurance results for all four
UAVs.
Table 6.3: Forward mode endurance test results.
UAV

Test #1

Time-to-25%-Battery
Capacity (mins)

Time-to-Auto-land
Warning (mins)

1

15

19

2

16

20

2

15

19

Average

15

19

1

18

23

2

20

25

2

20

25

Average

19

24

1

18

18

2

20

20

2

20

20

Average

19

19

1

N/A

8

2

N/A

8

2

N/A

8

Average

N/A

8

UAV 1

UAV 2

UAV 3

UAV 4

48

6.2.2

Hovering mode endurance testing

Hovering mode endurance testing was conducted indoors, in a room with all doors closed, to reduce theimpact of wind. In addition, the same battery and UAV were used for all experiments. Similar to the forward
mode endurance tests, the hovering mode endurance tests were divided into two parts. First, the time it took
for the UAV’s battery to drop to 25% of capacity was determined. This gave a measure of the UAVs
effective time, i.e., the time the bridge inspections can safely be executed. Second, the time it took for the
UAV to execute its auto-landing feature or sound warning to land when the battery level reaches criticallevels was determined. Each of these tests was performed three times and the average was taken. Table 6.4.
summarizes the results of the endurance tests.
Table 6.4: Hovering mode endurance test results.
UAV

Test #1

Time-to-25%-Battery
Capacity (mins)

Time-to-Auto-land
Warning (mins)

1

15

19

2

16

20

2

15

19

Average

15

19

1

18

23

2

20

25

2

20

25

Average

19

24

1

18

10

2

20

12

2

20

10

Average

19

10

1

N/A

8

2

N/A

8

2

N/A

8

Average

N/A

8

UAV 1

UAV 2

UAV 3

UAV 4

From both the forward mode and the hovering mode tests, it was observed that for UAV 3, as soon as
the battery capacity reaches 25% the "land now" warning is activated. This explains the reason why thetime to "Time-to-25%-Battery Capacity" and "Time-to-Auto-land Warning" are the same. For UAV 4, it
was not possible to record the "Time-to-25%-Battery Capacity" because its battery management system did
not provide that information. Therefore, results of only "Time-to-Auto-land Warning" were recorded. It is49evident from the results that endurance is required to perform an inspection task, UAV 2 may be the best
choice.
6.3 Global Position System
The reliability and accuracy of UAV functionality relies on accurate location data provided by the GPS.
One of the main challenges of UAV-enabled bridge inspection has to do with the fluctuation of the GPS
signal during inspection in some more concealed locations, such as beneath the bridge deck. Therefore,
when bridge inspection is carried out in locations with fluctuating GPS signals, it is vital that UAVs withreliable and robust GPS are selected. The choice of UAV should be one that has a faster "time-to-recover",when there is a disruption in the signal. Unfortunately, GPS information is not provided in the specifications
document for most commercial UAVs. Therefore, the motivation for this test was to serve as a guideselecting UAV for GPS-critical inspection.
6.3.1

GPS time-to-recoverThis test was performed in stationary mode where the UAV is not flying. For UAV 2 and UAV 3, it waspossible to obtain the GPS information while the UAV was stationary. UAV 1 was not tested because its
GPS information is only available in-flight mode. UAV 4 does not have GPS support. This may seem to bea disadvantage. However, this can prove to be an advantage when the UAV is in consideration for inspection
in a GPS-denied environment. Table 6.5. summarizes the results for the GPS for UAV 1 and UAV 3. The
results indicated the GPS recovery time for UAV 3 is superior as compared to UAV 2.
Table 6.5: GPS time to recover.
Time-to-recover (seconds)
UAV /Test #

1

2

3

4

5

6

7

8

9

10

Average

UAV 1

9.98

9.81

8.59

10.71

9.41

8.53

8.56

7.96

9.15

9.96

9.26

UAV 2

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

UAV 3

7.93

6.53

5.77

5.98

6.98

7.24

6.26

5.92

5.81

7.93

6.58

UAV 4

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

6.4 Autonomy
In this section, we present the results of the three autonomy tests namely, auto-land, waypoint following,
and return-to-home. From these tests it was observed that only UAV 3 supported all the autonomous
functionality. UAV 1 and UAV 2 supported auto-land and waypoint following, but not return-to-homefunctionality. UAV 4 supports none of the autonomous functionality. Table 6.6. summarizes the results ofthe test.
Table 6.6: Autonomy test results

50

6.4.1

Platform

Auto-Land

Waypoint following

Return-to-Home

UAV 1

Yes

Yes

No

UAV 2

Yes

Yes

No

UAV 3

Yes

Yes

Yes

UAV 4

No

No

No

Illumination

Results of the illumination experiment were obtained for only three UAVs, because at the time of theexperiment, the UAV 4 had crashed. Each row of Figure 6.8 shows how the UAVs’ camera performs insimilar light conditions. As explained before, level 4 indicates that lightning level is maximum, and level 1
indicates minimum lightning. It is evident from the results that the ability to detect the defects in the testelement decreases with visibility. The results show that UAV 3 has superior camera performance ascompared to UAV 1 and UAV 2. Also, we studied the proximity of the UAVs to the test element under
different lighting conditions affects the ability to detect defects in the test element. It was observed that thecamera performed poorly when larger distances existed between the UAV and test element. This madedetecting defects impossible in the low visibility scenarios. The results highlight the need to fly as close as
possible to an element under inspection.

Figure 6.8(a): Illumination level 4 (> 250 lx)

Figure6.8. (b): Illumination level 3 (220 – 250 lx)

51Figure 6.8.(c). Illumination level 2 (120-220 lx)

Figure 6.8. (d). Illumination level 1 (10-130 lx)
The entropy of the images captured for the various images at the different illumination levels was also
computed. The observations were made by visually inspecting the images’ match with the entropy
calculations. UAV 3 had a higher entropy across all levels as compared to the entropy of UAV 1 and UAV
2.
The entropy of the images captured at the different illumination levels was also computed, with thematch between each image and the entropy calculations observed. UAV 3 had a higher entropy across alllevels as compared to the entropy of UAV 1 and UAV 2.
To corroborate the results obtained from the lab environment with field conditions, the team attempted
to replicate a similar experiment in an actual bridge environment. Since the illumination level at the bridge
site could not be controlled, one column of the bridge with different illumination conditions at each of thefour sides was selected. It is worthy of note that the field experiment was impacted by the prevailing wind
gusts, thus the images captured, although similar, may have slight wind-induced variations. Furthermore,this field experiment was conducted using only two UAVs (UAV 1 and UAV 2) because operating UAV 3
in the presence of wind gusts in GPS-denied environments was determined to be too challenging. Although
it was impossible to measure the illumination level at the four faces of the column, based on visualinspection, the illumination levels were labeled Level 1, Level 2, Level 3 and Level 4. Similar to the lab
tests, level 1 corresponds to the lowest illumination condition, with level 2 and level 3 being intermediatelevels and level being the highest illumination level. The results of the field experiment are shown in Figure
34. Similarly, it is evident by visual inspection that UAV 2 performed better than UAV 1, which
corroborated the result from the lab environment.

52

(a) UAV 1(b) UAV 2
Figure 6.9: Illumination Level 4(a) UAV 1(b) UAV 2
Figure 6.10: Illumination Level 353

(a) UAV 1(b) UAV 2
Figure 6.11: Illumination Level 2(a) UAV 1(b) UAV 2
Figure 6.12: Illumination Level 1

The entropy of each of the images acquired by UAV 1 and UAV 2 was computed under the different
illumination levels. Here also, the results are consistent with the lab environment results. It is observed from
Table 13, in all the cases, the entropy of the images captured with UAV 2 is higher than that of UAV 1.
Table 6.7: Entropy of Images for Each UAV (Bridge Site)

6.4.2

Platform

Level 1 (Dark)

Level 2

Level 3

Level 4 (Bright)

UAV1

6.96

6.95

7.41

7.61

UAV2

7.38

7.40

7.53

7.58

Vibration

The vibration test was executed with UAV 1 and UAV 2. This experiment could not be performed with all54

of the candidate UAVs because UAV 3 had difficulties taking successive images with an interval of about
1 second. Also, UAV 4 was not available at the time of the experiment due to the aforementioned crash. As
described in the experiment section, two images were captured at intervals of about 1 second, and thisprocess was repeated 14 times. For each of the 14 two-second interval images, the first image wasconsidered as the reference and the peak-signal-to-noise-ratio (PSNR) between the reference images andthe second image was computed. Table 6.7 shows the noise that was introduced as a result of the vibration
for UAV 1 and UAV 2. It is observed that the PSNR of UAV 2 is, on average, lower than the PSNR of
UAV 1. It thus indicates the gimbal of UAV 2 is more stable in the presence of vibration than that of UAV
1.
Table 6.8: Vibration impact on PSNR
Platform

Average

UAV 1 16.59 16.92 16.42 15.79 19.21 22.74 20.70 19.90 22.14 22.74

22.79

21.06 21.14

21.04

19.95

UAV 2 16.47 17.37 19.46 13.85 17.96 21.85 14.09 19.83 19.11 20.43

19.95

19.62 17.99

13.89

17.99

UAV 3

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

UAV 4

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

6.5 Wind Gusts Tolerance
From this experiment, it was observed that whenever the UAV flies in a direction perpendicular to the wind,a force is exerted on it which causes it to deflect away from the source of wind gust. This deflection whichis equivalent to the force exerted on the UAV is calculated by identifying the maximum UAV accelerationfrom the IMU logs. The impact of the wind gust on the UAV is computed using Equation 4.4. Table 15
shows the acceleration changes due to the wind gusts on UAV 1 and UAV 2. From the results, it is evident
that UAV 2 experiences the greatest impact when it flies past the fan. These results are consistent with theobservation of the pilot, who indicated he had to provide additional force to counteract the effect of thewind gust for UAV 2 as compared to UAV 1 in the pressure of the wind gust.
Table 6.9:Wind Gust Impact on Acceleration Change
Platform

Impact on Acceleration Change

UAV11090.40

UAV2

17049.41

55

6.6 Reassessing the selection of the UAVs
We started with selection, test, and evaluation of four UAVs among which UAV3 was found very
vulnerable in the presence of wind gusts in GPS-denied environments and UAV4 crashed early in the testing
program and wasn’t available for majority of the tests. Looking back in Table 3.11, we can also observethat the AHP process would suggest that UAV 1 and UAV 2 with higher scores, are more appropriate
choices for bridge inspection. Note that the AHP process was conducted to select UAVs even before
conducting the test and evaluation experiments, primarily based on the available information in catalogs,
technical manuals, and other available resources about the UAVs. In our particular case-study, mostexperimental results confirmed the superiority of UAV 1 and UAV2, which is consistent with the AHP
results summarized in Table 3.11, and hence, we do not need to revise the AHP weights. Excluding UAVs
3 and 4, the AHP-based scores of UAVs 1 and 2 are shown in Table 6.10 which are relatively similar.
Table 6.10: Scores of UAVs 1 and 2
UAV 1

UAV 2

Criterion
WeightCan look upward

0.05040.05040.2015

Gusts Tolerance

0.02720.0272

0.0926Autonomous

0.02510.02510.0801

Gyro

0.01460.0146

0.0584Ultrasonic

0.03020.00600.0483

Accelerometer

0.01170.01170.0466Wind Tolerance

0.01080.01080.0397

Endurance

0.01140.01910.0381

Defualt Payload

0.01160.00870.0376

All-weather ability

0.00780.00780.0311

Custom payload?

0.00680.00680.0273

Max Payload
weight0.00520.00520.0207

NavCam

0.00510.00510.0205

Net Weight0.00500.00500.0198

Number ofEngines0.00470.00470.0187GNSS

0.00420.00420.0169Compass

0.00380.00380.0152

56

Environmental
Temperature
Range

0.00190.00190.0148Communication
Link Range

0.00280.00280.0140RC Range

0.00280.00280.0140Magnetometer

0.00270.00270.0107

Max Airspeed

0.00190.00190.0074

Max Climb Rate

0.00130.00130.0066

Max Alt.

0.00160.00160.0062

Live stream

0.00150.00150.0058Beyond Line-OfSight Range

0.00140.00140.0056Frequencies

0.00120.00120.0048Low latency

0.00080.00080.0032

Encryption0.0003

0.00030.00030.0012

Final Score

0.27430.25491Note that the pair-wise comparison weights in the AHP process are subjective numbers, reflecting theopinion of the experts/testers. Hence, if after conducting test and evaluation experiments one decides toreassess the proposed AHP-based selection framework by revising the pair-wise comparison weights, only
Table 3.10 needs to be revised to update Table 3.11, assuming that the weights of selection criteria remainunchanged.

7 FIELD TRIALS
7.1 Test flights at Structure 620072
Structure 620072 in Moore County, NC was selected as an in-service bridge for test flights. This structure
was constructed in 1967. It crosses a water feature but has several spans over the land. This structure has
been used for a UAV demonstration previously, but only for flights beside and underneath the bridge.
Previous flights were not carried out above the bridge deck. A photo of this structure is shown in Figure7.1. This structure has been in service for several years, and currently exhibits significant distress,
particularly in the concrete pier caps, concrete columns, some retrofit steel members, and the concrete deck.

57

Figure 7.1: Overview of structure 620072 in Moore County.
Several tests were conducted in April and May 2021 to test the platforms including test of resilience toloss of GPS, quality of images and data collection. Prior to the flights, the inspection reports for this bridge
were reviewed. The most recent inspection report for structure 620072 (April 22, 2020) was obtained from
NCDOT’s Structures Management Unit. Using the inspection report as a guide, a punch list of elements
was identified prior to the test flights. These included locations identified as Priority Action Requests on
the inspection report, as well as other locations where distresses were documented. Effort was made to
select locations that included distresses of different severity levels, as well as distresses on different typesof elements. After performing the flights, the video was reviewed, and a separate video containing
highlights of the test flights was prepared.
Below, several still photographs captured from the video obtained by the UAV are shown. As can beobserved, the UAV effectively gathered images of the quality typically shown in many inspection reports.
Additional descriptions of the bridge elements, distress types, and site conditions are provided in thecaptions beneath each photograph.

58

Figure 7.2 Debris pile from high flow event piled
against column. Using imaging analysis technology,
the volume of the pile could likely be calculated toassist in debris removal activities.

Figure 7.4 Spalls on column, corroded longitudinal
steel. At closer magnifications of the image, thedeformations on reinforcing bars are visible, as wellas cracks alongside the spalled areas. Note thecondition of the channel bottom is also evident.

59

Figure 7.3: Debris pile from high flow event piled
against column. Using imaging analysis
technology, the volume of the pile could likely becalculated to assist in debris removal activities

Figure 7.5: Cracks on pier over longitudinal steel.
Staining evident, indicating initiation of corrosion.

Figure 7.6 Hairline cracks visible on underside ofconcrete deck.

Figure 7.8: Diaphragm with bolted and welded
connections shown, along with condition of concretepier cap. Condition of bearing pad, plate, and bolts
can also be observed.

60

Figure 7.7: Crack on underside of pier cap, following
longitudinal steel. Discoloration and staining evident
from drainage.

Figure 7.9: Steel plates bolted onto pier cap, withcorrosion evident. Condition of girder, diaphragms
and connections can also be observed.

Figure 7.10: Spall with exposed, corroded rebar inconcrete diaphragm overhang. Condition of drainage
pipes and utility conduit can also be observed.

Figure 7.11: Staining on underside of girders.

Figure 7.12: Staining and cracking in pier cap. Condition of girders and diaphragm is also evident.
Preliminary flight tests at structure 620072 were largely successful. Two different candidate UAVs
demonstrated adequate performance at a field structure in typical (to favorable) weather conditions. The
research team gained valuable insight into the necessary planning required to begin integrating these two
UAVs into the inspection workflow. Experience was gained regarding the flight plan, such as the numberof flights required to inspect a structure of this size/configuration/condition, the quantity of bridge
elements/area of bridge that could be reasonably inspected on each battery, amount of time lost to UAV
setup and non-inspection travel. Additional insight was also gained into the quality of images that could beobtained in different illumination conditions, geometric configurations, and camera angles. From aninspector’s standpoint, images captured by the UAV could be directly compared to those obtained during
the conventional inspection, providing confidence in their quality. Although not quantified or qualitatively
assessed, the distresses visible in the UAV-obtained video and images could be assessed per the inspection
protocol and compared to the ratings and quantities provided in the latest inspection report. It is noted,
however, that there was a time lag of over a year between the conventional inspection and these UAV fieldtrials. In conclusion, field trials at structure 620072 provided valuable insight into the work needed tointegrate the candidate UAV platforms into the inspection workflow, which will be phase 2 of this work.

61

8 IMPLEMENTATION AND TECHNOLOGY TRANSFER PLAN
Research Product 1

AHP UAV selection procedure

Suggested User

NCDOT Division of Aviation and Structures Management Unit

Recommended Use

The Analytic Hierarchy Process (AHP) methodology can be adopted for themultiple-criteria decision making (MCDM) and comparing the capabilities ofmultiple UAS platforms. The AHP methodology can be applied to different
criteria (in this case, 32 criteria) defined under major categories including flight
performance, situational awareness, payload and sensor capabilities, andcommunication quality. The procedure requires a pairwise comparison in ahierarchical manner at the category level, criterion level, and candidate platform
level. The results from comparisons are integrated to an AHP table and can resultin the selection of the most suitable UAS for bridge inspection in the defined
scenario. This should help NCDOT to systematically select UAS for bridge
inspection purposes based on the priorities and desired performances.

Recommended Training

None at this time.

Research Product 2

Methods of test and evaluation of UAVs for bridge inspection

Suggested User

NCDOT Division of Aviation and Structures Management Unit

Recommended Use

The designed experiments can be used to test and evaluate the UAVs for bridge
inspection as an effort for certification of UAVs for the agent-wise adoption of
UAVs. This should help NCDOT to develop a certification procedure for
adopting UAS for bridge inspection purposes.

Recommended
Training

None at this time.

Research Product 3

Weather Data Collected at the Bridge on US 70 Eastbound (structure 4) over the
East End Connector in Durham, NC

Suggested User

NCDOT Division of Aviation and Structures Management Unit

Recommended Use

Temperature and wind data over multiple periods of time and measurements at
multiple locations of the bridge structure were archived for future use.

62

Recommended Training
None at this time

Research Product 4

Initial Inspection Data Collected by UAVs at Selected Structures

Suggested User

NCDOT Structures Management Unit

Recommended Use

Review to inform progress on phase 2 of this work, aimed at integrating UAVs
into bridge inspection workflows and using UAV-acquired data to supportcompletion of bridge inspection reports.

Recommended
Training

None at this time.

63

9

CONCLUSION

As part of this project, a hierarchical MCDM framework development for UAS-enable bridge inspection
selection practices was introduced. The framework employed a hierarchical method to analyze 32 criteria
categorized in Flight Performance, Situational Awareness, Payload and Sensor Capabilities, andCommunication Quality. Using the AHP methodology, the significance of each criterion with respect to thebridge inspection was numerically quantified through pairwise comparisons. Furthermore, the AHP
methodology was implemented to quantitatively compare each of the candidate UAS platforms’
performance with respect to each criterion. The result of this methodology implementation on the candidate
platforms obtained a fair and comprehensive comparison of their suitability with respect to each criterion.
By incorporating available UAS platforms and user preferences and then scoring their overall capabilities,
the framework will allow the decision makers to select the most suitable UAS for different bridge inspection
practices. This initial UAS-enabled bridge inspection decision making framework can be applied to othersituations, as long as the pairwise comparisons meet the AHP consistency constraint.
The weather data gathered by the mounted weather station provided valuable information for thecomputational modeling and the design of experiments for the UAV flights. For this work only thetemperature information was not utilized but was archived for future researchers. The gathered wind data
included direction, average speed, and gust speed. The wind conditions at deck level provided boundary
conditions for the computational models. The wind conditions gathered at both locations under the bridge
confirmed the significant shielding the structure provides. The data also supported the simulation findingsthat the forces encountered by the UAV under the bridge results from the specific geometry of the bridgecomponents. The computational modeling of the bridge sections showed the increased velocity andturbulent energy is found as the air flow is directed through the diaphragms resulting in jet-like behavior.
Simultaneously, low force areas are found near the gusset plate and concrete bends where shielding occurs.
These large variations in air flow and resulting forces are important to the flying of the UAV.
Further, the advantages offered by UAVs when used as a tool for bridge inspection were explored. Fourfeature categories and their sub features that should be taken into consideration when selecting UAVs forbridge inspection were identified and discussed. In addition, simple experiments for testing and evaluationof various capabilities of UAVs by subjecting the UAVs to the various testing conditions were developed
and presented. This enabled the comparison of different commercially available UAV platforms based on
the needs of an inspection mission. Results indicated that the candidate UAVs have different performance
characteristics. Therefore, testing and evaluation is crucial in identifying UAVs that will perform effectively
under different bridge inspection scenarios. The tests developed as part of this work and described herein
could be modified and utilized in the future to evaluate new UAVs that may become available as this areaof product development is rapidly growing and advancing. Overall, out of four UAVs which were gone
through the developed T&E experiments, UAVs 1 and 2 were found more effective solutions for bridge
inspection particularly due to their ability to fly underneath of the bridge. This was confirmed both by an
AHP process conducted before the experiments for selecting the UAVs and through the experimental results
in a consistent way.
Preliminary flight tests at a 10-span bridge exhibiting significant distress were largely successful. Two
different candidate UAVs demonstrated adequate performance at a field structure in typical (to favorable)
weather conditions. The research team gained valuable insight into the necessary planning required to begin
integrating these two UAVs into the inspection workflow. Experience was gained regarding developmentof the flight plan and the logistics of inspection of a structure of this size/condition using the candidate
UAVs. The images captured by the UAV could be directly compared to those obtained during theconventional inspection, providing confidence in their quality. Distresses visible in the UAV-obtained
video and images could be compared to the ratings and quantities provided in the latest inspection report,
again providing confidence in the ability of the UAVs to gather specific data required to be included on a64

bridge inspection report. Overall, field trials at structure 620072 provided valuable insight into the workneeded to integrate the candidate UAV platforms into the inspection workflow, which will be phase 2 ofthis work.
Future Research Needs
Future research needs based on the outcomes of this project includes different dimensions as briefly
listed below:
•

•

•

•

UAV and payload setup:a. A set of checklists for calibration and set up the sensors and UAVs for bridge inspection.
b. Sensor fusion for detecting problem spots and their severity at different bridge elements.
Data archiving and processing:c.
A procedure for archiving huge data being collected during the UAV-assisted inspection.
d.
Data-analytic tools for initial assessment of UAV-assisted data.
Bridge and near-bridge analysise.
A systematic study of bridge element sizes and configurations in combination with amore extensive variation of weather conditions. This study would support thedevelopment of a guide that would provide fly/no-fly guidelines based on a set of criticalbridge information. This guide would allow the inspector and/or UAV pilot to obtainbridge specific information without the length process of model create and CFD analysis.
Workflow development for adopting UAVs for bridge inspection:f.
Guidance regarding identification of candidate bridges for UAV-assisted inspection (e.g.
characteristics of bridges that make them good candidates for use of a UAV to
supplement inspection vs. characteristics of bridges that make a structure less desirable
for use of a UAV to supplement inspection.
g.
Guidance to support integration of data collected by UAV into inspection reports. A
workflow (or workflows) to support UAV-assisted inspection, including logistics andconsiderations required for pre-inspection, during inspection, and post-inspection.

65

A

APPENDICESFORFINAL REPORTNorth Carolina Department of Transportation
Research Project No. 2020-23
UAV Selection Methodology and Performance Evaluation
to Support UAV-Enabled Bridge Inspection
By
Ali Karimoddini, Ph.D.
Associate Professor
Tara L. Cavalline, Ph.D., P.E.
Associate Professor
Beth Smith, Ph.D.
Assistant Professor
Rodward Hewlin
Assistant Professor
Abdollah Homaifar
Professor
Edward Mahama
Graduate Research AssistantNorth Carolina Agriculture and Technical State University
1601 East Market Street Greensboro, NC, 27411

March 2022

A.1 APPENDIX A – EXTENDED LITERATURE SURVEY ON TESTING AND EVALUATIONOF UAV-ENABLED BRIDGE INSPECTION
A.1.1 Introduction
Regular inspection is imperative to maintain the bridges for road safety and to lengthen the lifespan ofbridges. Like many states, NCDOT and their contracted partners perform thousands of inspections annually.
Due to resource reductions, time limitations, increased traffic, restrictions on lane closures, and otherfactors, there is the need for exploring the use of modern technology and equipment that can help to improvethe efficiency of the existing methods and mitigate the risks associated with the traditional bridge inspection
methods. Here, a discussion on the existing bridge inspection techniques and their challenges is presented.
It also introduces UAV-enabled bridge inspection and discusses its pros and cons. Finally, previous
contributions to UAV-enabled bridge inspection are also discussed in depth.
A.1.2 Conventional Bridge Inspection Techniques
Existing bridge inspection techniques are demanding, less efficient and can sometimes expose inspection
personnel to danger. They usually require special equipment such as cranes, snooper trucks, scaffolds andrope access. Figures A-1, A-2 and A-3 show the equipment in action for bridge inspection. Inspection of
the under-deck of bridges, requires snooper trucks to be stationed on the bridge deck. This means that alane will have to be closed for inspection, impacting free flow of traffic and causing inconvenience to road
users.
In addition, there are certain bridge components that are difficult to reach and therefore require rope
access for inspection. This can expose the lives of inspection personnel to danger. Also, specially trained
professionals such as industrial climbers are employed to access special parts of the bridge, but they usually
have little to no knowledge of component damages detected. Thus, they can only take photos and videos ofidentified defects to be analyzed later by civil engineers. Thus, real-time inspection of components is mostoften not possible.
Furthermore, in the traditional inspection techniques, defects detected are documented manually inreports. Inspection personnel typically use textual descriptions and photos to document observed defects.
The outcome of these recorded defects relies heavily on the experience of inspection personnel and can also
be easily affected by the mood of the inspectors.
In view of the challenges with the traditional inspection techniques, there has been a conscious effort tofind alternative inspection means or tools that can supplement the existing methods. UAVs seem to be a
viable tool that can mitigate most of these challenges. As such, several departments of the transportation(DOTs) across the United States have been exploring UAVs feasibility for bridge inspection. In the next
section, potential benefits of UAVs for bridge inspection are discussed.

A-1

Figure A.1: Figure A-1. Scaffolds [78].

Figure A.2: Snooper trucks [79].

Figure A.3: Rope access [80].
A-2A.1.3 UAS-Enabled Bridge Inspection
UAVs are small aircraft that can be flown remotely either in autonomous mode or in manual mode by apilot with the help of a controller. Traditionally, they were used solely by the military for Traditionally,
they were used solely by the military for combat and reconnaissance missions. However, in recent times
they are receiving an increasing level of attention for their use in a variety of civil applications such as,
remote sensing [81], search and rescue missions [82], disaster management [83], courier services [84],security and surveillance [85], wireless coverage [86], precision agriculture [87], and infrastructure
inspection [88].
The main drivers for the popularity of UAVs in civil applications are advances in UAV technologies,
driving the cost of UAVs down. Furthermore, in the last decade, there have been tremendous advances inremote sensing technologies such as light detection and ranging (LiDAR) and photogrammetry. The
combination of these two drivers have thus made UAVs attractive in many civil applications. In fact, bridge
inspections seem to be the one application area that is receiving attention due to cost and safety advantages
they offer. Several DOTS in the United States have been exploring the use of UAVs for bridge inspection.
Figure A-4 shows states that have in the past explored the use of UAVs for bridge inspection or is currently
investigating its use.

Figure A.4: State DOTs with current or past UASs exploration.
A.1.4 Benefits of UAS-enabled bridge inspection
The benefits that come with the use of UAV for bridge inspections are both technical and economic. They
eliminate the risk associated with the traditional bridge inspection methods such as rope access discussed
above. UAVs can be remotely piloted to inspect bridge components that will otherwise require roped accessand putting the lives of inspection personnel at risk [17]. In fact, safety advantages afforded using UAVs
were reported by experimenting with UAV-assisted bridge inspection by some DOTs [60, 76, 89]. In
addition, UAS-enabled bridge inspections are economical since no specialized equipment such as snooper

A-3trucks, scaffold, ladders, etc. are required for inspection [90]. Thus, data can be acquired without the needfor this specialized equipment resulting in reduced bridge inspection cost. Also, UAVs can also offer realtime bridge inspection. It is possible to mirror the screen of the pilot onto another screen which can beviewed by inspectors. The inspectors can thus take a snapshot of identified defects for further examination
later. Furthermore, it takes less time to execute an inspection task as compared to the traditional inspection
techniques [90]. With UAVs the use of specialized equipment is eliminated except in special instanceswhere UAVs cannot suffice. In scenarios where it is too difficult to reach bridge components, UAV takeless time as compared to roped access or snooper trucks [17]. Finally, all the remote sensing technologies
for bridge inspections are non-contact techniques that leads to minimal traffic disruption and require low
labor [88]. The collected data is a soft-copy and thus it makes the easy application of advanced data analysis
techniques.
A.1.5 Challenges of UAS-enabled bridge inspection
The main hurdle that restricts the use of UAVs for bridge inspection is government regulations. Current
Federal Aviation Authority (FAA) regulation for instance, allows UAVs to be used for bridge inspection
only if there is no traffic in sight [90]. Therefore, for bridge deck inspection, the road still needs to be closed
although no access equipment will be needed. There is also the restriction of beyond-line-of-sight flight[33]. The pilot of the UAV is required to always have visual contact with the UAV during inspection even
if the UAV has live streaming capability.
Another challenge is the UAV capability limitations [90]. This can range from the ability to operate ina GPS-denied environment [90], bad weather [17] and the flight time [33] of the UAV. The small size of
the UAVs makes them susceptible to wind gusts rendering them difficult to control during inspection [17].
Also, under deck inspection usually must be executed in manual mode since there is usually limited or no
signal under the bridges. In addition, most UAVs cannot fly continuously for more than 1 hour. This meansthat inspection must be stopped intermittently for the battery to be replaced which can sometimes be
inconvenient for large bridges. In addition, some bridge inspection tasks requiring physical contact, suchas determining the width of cracks, cleaning bridge elements to remove rust or dust for detailed defectobservation cannot be accomplished with UAVs [17]. Therefore, in those instances traditional methods will
have to be relied on. Furthermore, there is no one fit all UAV that can perform a complete bridge inspection.
For instance, a UAV equipped with an ultrasonic sensor may be suitable for bridge deck inspection,
however, components such as bearings that require close examination can be a challenge. This is because
ultrasonic sensors restrict the minimum clearance between the UAV and bridge. Thus, in such scenarios it
is required to choose a UAV that can get as close as possible to the bridge. Finally, there is no available
comprehensive guide on testing the various UAV criteria for a specific task. This is particularly importantfor inspectors to have a fair idea of how the chosen UAVs will perform when use for inspection.
A.1.6 Related works
There are several studies that have investigated the use of UAVs for bridge inspection. The forerunners ofmost of these studies are the state DOTs usually in partnership with academia.
In [33], UAV-enabled bridge inspection methodology is investigated. This led to the development of afive-stage bridge inspection workflow which are bridge information review, site risk assessment, pre-flightA-4

setup, UAVs-enabled inspection and data processing. The UAVs used were chosen based on flight time,
upward viewing camera capability, camera resolution, video resolution, payload capacity, illumination andcommunication range. Although the criteria are crucial for bridge inspection, they are limited. There aresome criteria that can greatly impact the effectiveness of the UAV for bridge inspection that was notincluded. For instance, the results of [91], showed that fluctuation in wind speed caused by atmosphericturbulence and body generated turbulence can impact the quality of images captured during inspection. In
addition, in [92], we showed that the presence of electromagnetic interference can impact the effectivenessof UAVs for bridge inspection. There are other crucial criteria such as live-stream capability, autonomy,
maneuverability etc. which were not taken into consideration in selecting the UAVs bridge inspection.
Furthermore, the selection of the UAVs was based mainly on the specification document provided by the
UAV manufacturers. This restricts the criterion upon which the UAVs can be assessed since some of theimportant criteria such as interference immunity is usually not specified in the specification’s material.
In [89], Minnesota DOT in collaboration with Collins Engineering reported their findings on thechallenges and benefits of UAVs for bridge inspection. This work was carried out using four different
bridges in order to evaluate the effectiveness of the UAVs for inspecting different types of bridges. The
selected bridges used in the study are long single span prestressed concrete bridge, open spandrel concretearch bridge, a five-span steel underdeck truss and five truss arch spans. Some of the challenges mentioned
in this work are the inability to use chosen UAVs to inspect the under deck of bridges and the inability ofsome of the UAVs to operate in GPS denied environments. They also reported scenarios where UAVs wereable to detect defects that were missed by the traditional inspection techniques. Also, they indicated therestriction imposed by FAA rule that prevents the inspection of bridges with traffic. They however reported
that safety and cost advantages far outweigh the challenges they encountered.
The Michigan Department of Transportation reported their results of the exploration of 5 different UAVs
for bridge inspection [60]. This work was conducted using a combination of payload sensors including
visual cameras, infrared cameras, and LiDAR sensors. They reported effectiveness of a particular UAV
depends on the bridge inspection scenarios. For instance, they reported that a UAV equipped with thermal
sensors was the most effective tool for delamination detection in bridge deck. In a nutshell, this work
recommended UAV use for inspection as they are cost and time effective tools for bridge inspection.
Florida DOT of transportation also investigated the feasibility of using UAVs as a supplementary tool
to traditional bridge inspection methods [89]. They indicated in their report that a UAV-assisted inspection
can lead to an improved cost of inspection, minimized inspector exposure to danger, increased public safety,
and facilitated effective data acquisition and storage. Also, they identified payload weight limitation,
stability of control system in wind gusty situations and deterioration of image quality in bridge areas withlimited illumination as the main challenges. One aspect of this work was the development of a systematic
decision-making toolbox for the selection of UAVs depending on the inspection requirements. Furthermore,they also estimated the minimum clearance the selected UAV can safely operate in prevailing wind gusts
between 11km/h to 16 km/h.
The authors of [93] proposed some basic guidelines regarding the use of Remotely Piloted Aircraft
Systems (RPAS) for surveys and inspection. One component of the proposal involved the technicalconsiderations and characteristics of the device for inspection. The technical consideration was mainly onultrasonic and camera capabilities of the UAV. While these two criteria are crucial for inspection, it is not

A-5adequate to decide on which UAS to be selected for bridge inspection. A UAV can be equipped with a goodcamera but without a good control system to tolerate turbulence around bridges, it may be ineffective forbridge inspection. There are other criteria highlighted in previous discussion essential for UAS-enabledinspection that need to be tested and evaluated.
The development and characterization of experimental testing of a multi-rotor UAS in wind tunnel ispresented in [94]. The setup was used to simulate the adverse wind conditions that UAVs are most likely
to encounter in bridge inspection conditions. This relied on portable fans for generating indoor wind
disturbances in order to examine the UAVs response to wind turbulence. The frequency content of the wind
generated during the experiment is analyzed to identify suitable excitation for UAV. The result of thisserved as a guideline for testing and selecting UAS likely to be used in wind turbulent conditions. This
work focused on testing just one criterion.
The previous studies that explored UAVs for bridge inspection placed less emphasis on the UAV
selection. Also, previous research that performed testing and evaluation of UAV capability have acomprehensive treatment of usually one feature. With regards to the selection, the authors relied on very
few parameters. In most cases no test was conducted by the authors to verify if the UAV can withstandbridge conditions. As a result of the sole reliance on information in the specification documents, importantcriteria such as RF immunity and GPS robustness is rarely taken into consideration during UAV selection.
Therefore, in this work, we pursue a holistic approach by first identifying a comprehensive list of important
UAV criteria for bridge inspection. We go a step further by coming up with simple experiments to test thesefeatures. We use a combination of qualitative and quantitative means wherever appropriate to report our
results.

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