37th International Symposium on Automation and Robotics in Construction (ISARC 2020)

Research and Development on Inspection Technology
for Safety Verification of Small-Scale Bridges
using Three-Dimensional Model
Kazuhiko Sekia , Koichi Iwasab , Satoshi Kubotac , Yoshinori Tsukadad ,Yoshinori Yasumuroc and Ryuichi Imaiea

EYESAY Co., LTD, Japan, Graduate School of Kansai University, Japanb
EYESAY Co., LTD, Japanc
Faculty of Environmental and Urban Engineering, Kansai University, JapandFaculy of Business Administration, Setsunan University, Japane
Faculty of Engineering and Design, Hosei University, Japan
E-mail: seki-k@eyesay.co.jp, iwasa-k@eyesay.co.jp, skubota@kansai-u.ac.jp,
yoshinori.tsukada@kjo.setsunan.ac.jp, yasumuro@kansai-u.ac.jp, ryuichi.imai.73@hosei.ac.jp
Abstract –In 2014, a regular inspection of public
infrastructure facilities (bridges, etc.) once every five
years be-came a legal system. Of the approximately
730,000 bridges nationwide, 530,000 are small-scale
bridges with a bridge length of 2-15m. These bridges
are often not easily accessible by inspection engineers.
Therefore, in this study, for the purpose of studying
inspection methods as an alternative to visualinspection for small-scale bridges, development ofinspection robots, examination of efficient methods
for creating 3D CAD data, inspection using 3D models.
We examined the results from four viewpoints, themanagement system of results and the extraction ofdamaged parts from photographs by AI. As a resultof the research, we conducted demonstration
experiments several times, arranged the scope oftechnology application according to the usage scene,
and clarified the definition of functional requirements.
This can be expected to contribute to the developmentof more efficient inspection, improvement of
productivity, safety, etc. in future small-scale bridges,
as a material for development for actual operation.
Keywords –Bridge Inspection; Structure from Motion; 3D
Damage Figure; AI Damage Detection

1

Introduction

In 2014, the Japanese Ministry of Land, Infrastructure,
Transport and Tourism (MLIT) put in place regulations
requiring periodic inspections of public infrastructure
facilities (e.g., bridges) once every 5years [1]. This meant

919

that municipally managed bridges that were notpreviously subject to regular inspections, were inspected
all at once. Many of these bridges are relatively small anddifficult to inspect. Provisions were often made for sitesthat could not be approached without risk to inspectors,
such as use of various inspection equipment or
adjustments of inspection timing, but even this wasinsufficient for some sites, resulting in “inspection
impossible” categorization.
There are about 730,000 bridges in Japan, of whicharound 530,000 are small structures measuring 2–15 m inlength. Many such small bridges are not easily accessible
to inspectors, making visual inspections according to thedesignated procedure impossible. In 2019, the MLIT
revised the inspection procedures, allowing them to be
based primarily on close-up visual inspections, andpermitting the use of new technologies forcomplementing or replacing close-up visual inspections
by inspectors [2]. These revisions allowed conditionaluse of drones or robots for inspecting conditions at sitesthat are otherwise difficult to approach. Further, there areplans to develop guidelines on the use of newtechnologies, which requires the development of efficient
inspection technologies and investigation into their scopeof application.

2

Objectives and Methodologies

In this study, we conducted proof-of-concept
experiments targeting various issues and risks for small
bridges with the aim of investigating the scope of
application of technologies according to usage scenarios,
clarifying functional requirement definitions, andproviding material for developments toward actual

37th International Symposium on Automation and Robotics in Construction (ISARC 2020)

operations.
We develop a robot for use in place of human visualinspections at sites that cannot be entered, or where entry
is possible, but space is insufficient for inspections. Sucha robot would likely be effective for application tovarious civil engineering structures, but in this study, we
focus on its application to small bridges.
The implementation plan and focus of this study are
as follows. To investigate and extract various issues andthe scope of application, we conducted proof-of-concept
experiments such as robot operation tests, dataacquisition via mounted equipment, and post-processing
tasks using the acquired data, and provided comments
regarding their implementation.

2.1

Figure 1. Status of inspection work on small
bridgesDevelopment of an inspection robot

Concerns surrounding safety, labor shortages in theconstruction industry due to low birthrates and the aging
population, and a shortage of engineers in local
governments have prompted active research anddevelopment of inspection robots for the purpose of more
economical inspections. The MLIT has publishedmaterials on inspection support technologies [3], but
most are related to the use of drones and target relatively
large bridges. In this study, we developed a robot capableof entering beneath bride girders in place of a human.
2.1.1

Conditions

Based on our knowledge accumulated over 20 yearsof experience in bridge inspections and surveying tasks,
we set the primary specifications for the robot as follows.
A height of 70 cm is sufficient to allow human inspectors
to enter the space beneath bridge girders but make it
difficult to look up at the girders (slabs) that are the target
of inspections. Figure 1 shows the state of inspection
tasks for a bridge of the same scale as the bridges targetedin this study.




2.1.2

the mounting position of the mounted devices, and thenwe varied and investigated types of mounted devices,
their mounting positions, and settings for data acquisition
methods. Figure 2 shows the robot developed in thisstudy, and Table 1 lists its primary specifications.

The robot must be capable of entering and exiting a
70 cm space beneath bridge girders.
It must be possible to mount cameras or otherdevices capable of recording damage.
The robot must be capable of traversing over small
rocks, mud, and other surface irregularities of up to
several centimeters in size.
Implementation plan and focus

In developing the robot, we first selected usage
scenarios for various issues such as expected operational
environments, communication environments, and robot
operations under low visibility situations. We next
investigated what equipment should be installed. In
proof-of-concept experiments, we first verified factors
such as the operational performance of the robot, wirelesscommunication quality between it and its controller, and920

Figure 2. Developed robot and 3D CAD
Table 1. Primary specifications of the robot developedin this study
Specification
Size
WeightTransmitter device
Receiving device
Tire
Athletic
performance

2.2

Value
Width: 498mm/Depth: 592mm/Height: 375mm
4.0kg
Futaba 14SG
COOLTECH R7008HV
140mm Off-road tires
4WS

Efficient method for 3D model data
creation

The MLIT has been carrying out common
information model (CIM) tasks since 2012 and has
confirmed information sharing and safety improvement
effects among stakeholders using three-dimensional (3D)
models during infrastructure design and at construction
sites. Based on the CIM model creation specifications,
Yamaoka et al. [4] , [5] verified usage methods inmaintenance management using precise CIMs created at
the design stage. Further, Shimizu et al. [6] developed asystem for managing photographs taken during
inspections in a 3D model. They also proposed a method
for constructing 3D models by parametric modeling, andthis method is highly effective for newly creating 3Dmodels of existing bridges that are undergoing37th International Symposium on Automation and Robotics in Construction (ISARC 2020)

maintenance and for which models do not exist. However,humans or drones generally take photographs in thesecases, and there are no examples of creating 3D models
from photographs taken beneath the girders of small
bridges using an autonomous robot.
In this study, we therefore investigated methods bywhich data obtained from a robot entering the space
beneath bridge girders can be easily and efficiently
applied to creation of 3D model data. In bridge
inspections as well, there are increasing opportunities forconverting 2D image data into 3D point group data
through “structure from motion” (SfM) processing,allowing creation of 3D model data. Specifically, a fixedlaser scanner is used to interpolate complex forms that
could not otherwise be acquired. These are conditions
like those of the small bridges targeted in this study.
2.2.1

Implementation policy and focus

As an initial point of interest, it is important to acquire
still images with sufficient overlap for performing SfM
processing. We performed proof-of-concept experiments
to investigate how such images can be acquired not byhumans, but by robots. Specifically, we established photoacquisition methods (for still images and video), photo
resolutions, overlap ratios, distances to the target
structure, view angles, and the presence or absence offeature points, and evaluated accuracy for point clouddata generated through SfM processing under various
environments.
For our second point of interest, we created 3Dmodeling data from the generated point cloud data.
Currently, computer-aided design (CAD) engineers mustselect (click) arbitrary points among point cloud data tocreate line segments and surfaces based on theirexperience and knowledge of bridge construction andcombine these to create 3D model data. This is abottleneck because the process requires huge amounts oftime and labor. We therefore investigated methods for
performing this task more efficiently and with less effort.

2.3

Table 2. System function Lists
No
1
2
3
4
5

Conditions

Bridges are built in various forms, but we limit our
focus to girder bridges with reinforced concrete slabs andbox culvert bridges, which account for the majority ofsmall bridges.
2.2.2

by Unity Technologies that can handle 3D models.
Table 2 shows the performance requirements. It is
important to confirm the system’s use as a platform thatallows information sharing between stakeholders in
different positions.

Inspection results management using 3Dmodels

We constructed a prototype support system for 3D
damage map creation as a management method for
accurate and reliable sharing of structural lifecycle
information among facility managers, inspectors, andrepair workers, and investigated its necessary functional
requirements. As the development environment, we used
Unity [7], an integrated game development environment

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6

2.4

Function
Bridge Specific Data Registration
Specifications
DataofbridgeRegistration/Browse/Editing
Data SearchView and Operation of 3D model of bridge
DamageDataofbridgeRegistration/Browse/Editing
Narrowed-down
displayofdamageinformation(Part of Member/Damage Kind/Damage Rank
level)

Application of AI to detection of damage
in photographs

We investigated conditions for application andoperation of artificial intelligence (machine learning) to
extract training data for automatically discoveringdamage from among the huge amounts of image dataacquired by robots that are unable to distinguish between
soundness and abnormalities.
Inspectors apply machine learning using as training
data photographs of damage taken as in conventional
inspection methods from near the structure, thereby
creating a discriminator. This discriminator is used to
identify damage in photographs taken by the robot andconsider the results.

3

Implementation

3.1

Development of the inspection robot

3.1.1

Mounted equipment

Primary mounted equipment consists of a camera for
robot operations, a camera for photographing damage(we compared two models, a Panasonic GH5S and a
GoPro Hero7), and LiDAR (Velodyne VLP-16 Hi-Res)
for position tracking.
3.1.2

Robot dimensions, maneuverability, andoperability

Table 1 shows robot data, such as its dimensional
specifications. The requirements call for an ability toenter beneath a bridge girder of height 70 cm and toacquire images of its underside, so as to ensure a constantview angle; the design is for a 15 cm distance from the37th International Symposium on Automation and Robotics in Construction (ISARC 2020)

tire contact surface to the baseplate on which theequipment is mounted. We must also consider possible
obstacles to the robot’s undercarriage as it advances
beneath small bridges, such as sediment (mud and sand),gravel, pebbles, dead leaves, and vegetation. Therefore,
to improve its operability and obstacle avoidance
performance, we adopted four-wheel steering (4WS), a
steering method that allows independent setting ofsteering angles for each wheel of the robot. Further, we
set the tire diameters to 140 mm to allow operation insmall amounts of water. We attempted use of continuous
track propulsion in preliminary experiments but leaves
and twigs frequently became caught in the treads,
immobilizing the wheels, so we adopted tires instead. For
the battery, we used a small, lightweight, high-power
lithium polymer battery, and allocated sufficient space inthe robot’s middle for mounting a battery with capacityfor 1 hour of continuous operation. In terms of imagetransmission between the operator and the robot that hasentered a narrow space, we confirmed that transmissions
were not interrupted from beneath the target bridges withwhich we conducted our experiments. We further
confirmed that image transmissions were uninterrupted
during gradual movement of the operator’s position
about 20 m upstream and downstream from the center ofthe road surface and beneath the bridge. We did notconduct tests from farther distances because 20 m wasconsidered sufficient for the small bridges targeted in thisstudy.
3.1.3

Proof-of-concept experiment I

We performed this experiment with a bridge of length
2.0 m, width 4.5 m, and 80 cm clearance beneath girders.
We confirmed positioning of onboard devices, the qualityof SfM processing results using acquired images, and thepossibility of position tracking by the LiDAR. Tables 3
and 4 list parameters investigated in each proof-ofconcept experiment.
When investigating parameters related to SfM
processing, we used two camera models to confirm
differences in camera image quality and view angle. We
also confirmed differences arising due to turning the highdynamic range (HDR) setting on or off in each
installation direction and when using or not using wideangle mode. To investigate whether substitution of theequipment used for normal inspections as a photocontrol
point improves the results of SfM processing, we also
used the presence or absence of photocontrol points as aparameter.
When setting LiDAR-related parameters, to verify
any differences due to the incident angle of the laser on
the structure and the reference object, we investigatedthree horizontal installation angles, using the presence orabsence of a reference object as a parameter.
Table 5 shows primary specifications for the bridge

922

used in this proof-of-concept experiment, and Figure 3
shows a photograph of the proof-of-concept experimentbeing carried out. Figure 4 shows the installation angles.
Figure 5 shows conditions for camera installation
directions.

Panoramic view (bridge1/ bridge2/ bridge3)

Side view (bridge1/ bridge2/ bridge3)

Material installation status(aluminium staff/ white line/ eslon tape)Figure 3. Demonstration work status

Horizontal direction

Vertical direction

Figure 4. LiDAR installation angle

Figure 5. Camera installation directions

SLR camera at Bridge1 Action camera at Bridge1
Figure 6. SfM processing results

37th International Symposium on Automation and Robotics in Construction (ISARC 2020)

The results of this experiment showed that when
performing position tracking using the LiDAR, thebridge form was not reproduced in any case where thereference object was not installed on a side face of thebridge. Also, form replication tended to improve whenthe LiDAR was installed horizontally. This is likely
because the ability of a laser to capture the installedreference well is more effective for shape reproduction
than for capturing the bottom surface of slabs.
However, installing plates for use as reference objects
during inspections would be labor intensive and timeconsuming, making inspections highly inefficient.
Further, wind could blow plates over, which is a potential
task hazard.
We next confirmed generation of 3D forms throughSfM processing using robot-acquired image data. Figure
6 shows the results of SfM processing using image data
for the same bridge as acquired by a single-lens reflex(SLR) camera and an action camera. The SLR camera
could partially capture forms before entering beneathgirders but capturing structures beneath the girders wasnearly impossible. In contrast, the action camera could
capture the entire form, including beneath girders. Due tothis difference, there were many omissions in point clouddata created using the SLR camera. One effect likely
influencing this is the difference in brightness whenentering the dark environment beneath the bridgecompared with the bright environment outside. Figure 7
compares still images captured by the SLR and action
cameras. The action camera automatically and
immediately changes brightness settings when entering
beneath the girder, allowing capture of clear images
while operating there. It is of course possible to change
brightness settings for an SLR camera, but considering
operability during inspections, the ease-of-use of anaction camera likely makes it better suited to inspection
tasks. Such cameras are also superior in terms of theirsmall size and low weight.
Table 3. Study parameters (SfM)
CameraType

ValueSingle-Lens Reflex(SLR) camera
Action camera

Installation
direction

Vertical / Horizontal

Shooting
methodStill Image / Video

Control
pointWith or Without

CameraSettings

Wide angle mode:
On / OffHDR mode: On / Off

Note

Shooting
interval
3 CasesEslon
tapepaste on Deckslab bottom923

SLR camera

Action camera

Figure 7. Comparison of still images from singlelens reflex cameras and action cameras
Table 4. Study parameters (LiDAR)
Value
Installation
direction
Reference object(Board)Reference object(White Line)
Reference object
Shape/Size

Note

Horizontal/15°/45°
With or Withoutchangedistance

With or Without
Aluminium
staff/board/man

changedistance

Table 5. Bridge Specification
No

LengthWidth

Height ofunder thegirder

1

2.0 m

4.5 m

85 cm

2

2.2 m

4.0 m

70 cm

3

13.0 m

1.8 m

85 cm
(Inner)3.1.4

Structure type
RC Deck SlabbridgeRC Deck SlabbridgeConcrete Box
Girder bridge

Proof-of-concept experiment II

Based on the results of proof-of-concept experiment I
that concerned LiDAR-based position tracking, in proofof-concept experiment II we verified whether equipmentused in conventional inspection tasks or inspectors
themselves could substitute for the reference object. To
confirm any influences of surface shape or size of the37th International Symposium on Automation and Robotics in Construction (ISARC 2020)reference object, we investigated cases where analuminum staff and an inspector were used as reference
objects. Figure 8 shows a photograph of the installedreference object.
In proof-of-concept experiment II, we set
investigated parameters with a focus on photography
conditions for the action camera, and we considered what
effects these might have on the generation of 3D forms.
To confirm the conditions inferred from the results ofproof-of-concept experiment I, we set investigatedparameters in consideration of improved operability,
efficiency, and safety of inspections, confirmed whether
similar trends were reproduced in experiments performedon two bridges of similar type, and investigated thevalidity of the inferred conditions.
The results suggested that parameters minimizing
defects in the 3D form were a fisheye lens mode,
horizontal installation direction, and illumination turnedon. No differences were seen between still images andvideo capture. Because video capture has improved onsite operability, and clipping intervals can be adjusted forstill image extraction in post-processing, video capture is
likely most appropriate.

Board

Man

(TIN), finally converting data into a specified CAD
format.
Table 6. Process overview and examination/process
details of each processNo.
1

Process
Point
cloud
data
pruning2

Normalvectorclustering

3

3Dlabeling

Removepoint clouddata notsubject tomodeling

4

Planarfitting

Generate
fittedplanes foreach label

5

Edge
extraction

Generatesurfacemodel
vertices

6

Surfacemodeling

Generatesurfacemodel
fromgeneratedvertices

Aluminium staff

Overview
Reducepoint clouddata
acquiredby SfM
Group theresultingpoint clouddata foreach
generated
plane

Figure 8. Installed reference object

3.2

Efficient methods for 3D model data
creation

In LiDAR-based form measurements beneath a
bridge girder was possible to capture the form of bridge1
and bridge2, but not bridge3. This was likely because it
was impossible to maintain continuous recognition of theinstalled reference object.
We constructed an algorithm for generating 3D model
data from 3D point cloud data. Table 6 describes an
overview of processing, investigation content, andprocessing content in each step. First, we prune the point
cloud data to improve processing speed. We next perform
clustering to estimate planar regions in the target object.
The existence of point cloud data other than thosebelonging to the modeled bridge lowers modelingprecision, so we remove such data and perform 3Dlabeling before processing for planar-surface fitting.
When planar surfaces are formed, their intersections withother planes are taken as edges. We created an algorithm
that creates a surface model from the vertices generated
up to this point, and then a triangulated irregular network

924

Investigation/Processing
Pruning processing issped up by using anoctree data structure.
Generate planes frompoints in a given space by
the least squares method
and perform clustering by
the K-means method
using the angle formedby the Z-axis and thenormal vector for eachplane.
For all spaces in thespace octree, merge
connectable grids(labeling by distance),leaving only spaces
containing point cloud
data, and remove labelsthat do not correspond tosome minimal volume.
Use the least squares
method for fitting. Planes
determine the outerregion within the range
including the projected
points.
Search for intersections
between a given planeand other planes; whereintersections are found,
update boundary asedges.
Generate a TIN using
Delaunay triangulation.
Convert the TIN into a
specified CAD format
and output.

To confirm the utility of the algorithm, we performed
trials using highly accurate point cloud data as
verification data and confirmed that the 3D model data
were generated as expected. We used a ground-basedlaser scanner (Focus S350; FARO Technologies, Inc.) togenerate ideal point cloud data for verification. Further,
we performed SfM processing using image data acquiredby the robot, performed verification using the generated
data, and were thereby able to construct a model. By
calculating root mean square values for planes in the37th International Symposium on Automation and Robotics in Construction (ISARC 2020)

generated 3D model data and the point cloud data, weconfirmed that accuracy was within the LiDAR catalog
value of ±0.030 m. This can be considered as sufficientaccuracy for 3D model data applied to the maintenancemanagement that is the objective of this study.
No method for quantitatively evaluating SfMgenerated point cloud data has been established.
Therefore, there is need for performing various
verifications in the future to establish evaluation methods
and confirm robustness in actual operations.

3.3

3D model-based inspection
management system

results

We constructed a prototype support system for 3D
damage map creation to allow stakeholders to intuitively
and accurately grasp and share locations and states ofdamage and identified and organized application ranges
and issues for various parties.
The Windows application maintains a database ofbridge specification data, damage information, anddamage site ID numbers. The Unity application displays
3D model data on a screen. This system has functions forsuperimposing damage data on model data and for
confirming and editing damage information.
The system is designed to maintain minimal
information on bridge specifications, assuming there arecomponents such as a ledger system already managed by
the local government. Similarly, for inspection
information, we prepared a database table using adamage location identifier as its key to represent damage
locations, assuming a connection with an existing system.
Table 7 lists the main issues as indicated by persons
responding to a survey that involved 200 visitors to an
exhibition. Figure 9 shows the main screen of theconstructed prototype system and the damage data input
screen. In future work, we will develop a mechanism ordevice that allows efficient association of damage sites
on the 3D model data.
. Many small bridges in Japan have a low
maintenance cost per bridge. Therefore, the current
situation is that there is almost no cost to newly create 3Dmodel data. How to efficiently create 3D model data isimportant.
In this study, it was found that the structure and shapeof a small bridge is simple, and it is difficult tounderstand the damage situation if a damage map isexpressed by a 3D model.
In such a case, the use of CAD data as 3D model data,rather than using CAD data with colored point cloud dataor textures attached, improves the grasp of damage
conditions.
In future R&D, it is necessary to add a function thatcan read such data to the system.

925

Figure 9. System screen and damage dataregistration/browse screen.
Table 7. Main issues as indicated by persons
Role of each
persons
Facility
manager

BridgeInspectors
Repairworkers

3.4

Main issues
Support function to make an optimal
maintenance plan.
Interoperability with the existingroad management system (ledger
system).
Output function for inspection
reports.
Automatic registration of damage
location.
Optimal repair method andconstruction planning support.
Automatic
calculationof
construction quantity.

AI application to identification of damage
sites from photographs

We investigated algorithms and prepared anenvironment for introduction of AI methods. For training
data, we used photographs previously taken by theauthors or other inspectors judged as indicating damage.
For damage photograph labeling, we compared casesof labeling performed by inspectors and those byinexperienced persons provided with samples.
The results of discrimination in robot-acquired
images included misidentification of cracks, peeling, andrebar exposure, an overall lack of recognition of peeling,
and differences in scopes of occurrence. The results thus
included many misidentifications of locations, scopes,
and types of damage to be extracted. However, we
obtained knowledge about constructing a learning
environment and learning data, which enabled
identification of requirements for condition setting. The
following are items we identified as being particularly
important development policies:

37th International Symposium on Automation and Robotics in Construction (ISARC 2020)









4

Ensure consistent labeling of training data andprepare learning data that are high-quality(annotated) as well as abundant. Document how
inspectors think for conversion from tacit to formal
knowledge and identify what should be learned andhow.
Clarify relations between the required learning datascale and classification performance for each target
damage class (e.g., damage type and degree) to be
identified.
Develop and formulate tools, interfaces, andoperation processes that are linked with tasks so thatlearning data can be collected without placing
burdens on primary tasks.
Manage training data not as individual image files
but matched with bridge specifications and otherrelated information.

Conclusion

In this study, we summarized the requirements for
robot development that can enter the girders of small
bridges and acquire necessary and sufficient data forpost-process work from the viewpoints of technologicaltrends, development and maintenance costs, andversatility.
We have developed a robot that enters and exits under
the girder of a small bridge with a space below the girderheight of 70 cm or less. In addition, we performed acomparative verification of the conditions andinstallation methods of the equipment mounted on the
robot and proposed an efficient method for 3D model
data.
Regarding the 3D damage map creation support
system, the system requirements and issues wereorganized from the perspective of the relevant parties(facility managers, inspection operators, repair work
operators, etc.). In addition, it is also important topropose activities to replace the current 2D drawing
management method with the proposed method.
Regarding damage extraction by AI, the requirements
for a series of maintenance up to the introduction of AI
have been arranged.
In the future, further verification in various bridge
types and under girder environments will be required for
actual operation.

Acknowledgements
This work was supported by JKA and its promotion
funds from KEIRIN RACE, which was entrusted by the
New Media Development Association.

926

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