ANNUALREPORT

2021-2022
For the year ended March 31, 2022

Railway Technical Research InstituteForeword
Ikuo WATANABE
President of the Railway Technical Research Institute

I am pleased to announce that RTRI Annual
Report 2021-2022 is now available. This reportoutlines and introduces the organization of RTRI,
our master plan, research and development andother activities and related data of many research
fields. It would be our great pleasure if you could
glance over our progress in fiscal 2021 and it
helps deepen your understandings of RTRI.
Tough times also continued in fiscal 2021 forrailway businesses. The demand for railway
transport remained low due to lingering Covid19 pandemic and a series of natural disasters
caused serious damage to railway facilities inmany places. Furthermore, as a law to promote
global warming countermeasures was enacted
in May 2021 in order to attain a carbon neutral
society in 2050, decarbonization is now an urgent
issue for railways to be addressed.

lines. As usual, the researchers at RTRI quickly
conducted damage surveys and supported therestoration work by sending cross-sectoral teams.
In 2021, RTRI celebrated its 35th anniversary.
Under the vision “we will develop innovativetechnologies to enhance the rail mode so that
railways can contribute to the creation of a happier society”, we will continue to create quality
research outcomes by fully utilizing our capacity
as an institute covering all the fields of railway
technologies, in order to meet expectations byrailway operators and customers. Continued support and advice from all the rail-related people will
be greatly appreciated.

Under these difficult circumstances, however, wewere able to steadily implement all the planned
research and development projects and otheractivities according to the business plan for fiscal
2021, the second year of the 5-year master plan
RESEARCH 2025. While activities continued tobe restricted in all aspects of the society, due
to the pandemic, we have raised efficiency of
research work, improved our data and information infrastructure to facilitate working from homeand remote working. Thanks to these efforts toprevent delays in our activities, 261 research anddevelopment projects were implemented and 75
of them were completed by the end of fiscal 2021.
In August 2021 and March 2022, a heavy rainfall and the earthquake in the coastal area of
Fukushima prefecture caused damage to railway
Annual Report 2021-20221

Overview

Contents

Organization

2 Overview
Results of
4 MajorResearch and Development

30 News Release2

Board of Trustees

Board of Directors

Auditors

Annual Report 2021-2022

Railway Technical Research InstituteExecutive
Vice
Presidents

ChairmanPlanning Division

VehicleTechnology Division

Compliance Division

Structures
Technology Division

President
Executive
Directors

Administration Division
Miyazaki Test
CenterPower Supply
Technology Division
Track
Technology Division

Accounting Division

Disaster Prevention
Technology Division

Research &Development
Promotion Division

Signalling andOperation Systems
Technology DivisionInternational
Affairs

Information andCommunication
Technology Division

Wind Tunnel
Technical Center
Yamanashi
Maglev
Test Center
Marketing & Business
Development Division
Health Insurance
Systems Center

Materials
Technology Division
Railway Dynamics
Division
Environmental
Engineering Division
Human Science
Division

Railway Technology
Promotion Center

Maglev Systems
Technology Division

Railway International
Standards Center

Center for Railway
Earthquake Engineering
Research(As of April, 2022)

Annual Report 2021-2022

3

Major Results of
Research and Development
IMPROVEMENT OF SAFETY
1. Earthquake early warning method for the earthquake source
fault regions• We developed an earthquake warning method that reflects the amplification characteristics of seismic
motions at different locations and issues a warning when the P-wave exceeds a threshold specified.
• S-wave, which represents the principal motion, is predicted in real-time by multiplying the observed
P-wave by the S-wave/P-wave amplitude ratio at various locations prepared in advance. A warning
is issued when the ratio exceeds a threshold specified.
• A warning signal can be output in as little as one second or even less in the earthquake source fault
regions.
With current P-wave warnings in railways, it takes at least
one second to issue a warning after the earthquake isdetected, because data for over one second is needed
to estimate the epicentral distance and back azimuth.
To improve the immediacy of alarms for the earthquake
source fault regions, we developed a method capable ofissuing a warning in less than one second after earthquake
detection based on when a specified P-wave threshold isexceeded.
The proposed method determines whether an alarm isnecessary based on a real-time prediction of the amplitudeof the S-wave, which represents the principal motion. Thisprediction is multiplying the observed P-wave amplitude by
the S/P-wave amplitude ratio in advance (Fig. 1). The S/Pwave amplitude ratio varies widely from station to station,
however (Fig. 2). Thus, the proposed method improves

warning accuracy by using the S/P-wave amplitude ratio
determined from data at a variety of seismic observation
stations. Furthermore, since the method relies only on
P-wave amplitude at seismic observation stations andsimple arithmetic processing, it enables more immediate
and reliable earthquake early warnings in the earthquake
source fault regions.
Application of the proposed method to the 2016 Kumamoto
earthquake confirmed that the method is capable of issuinga warning faster than any other current P-wave warning
system in the earthquake source fault region (Fig. 3). The
adoption of this proposed P-wave “specified thresholdexceeded” earthquake warning into warning systems along
railway lines can significantly enhance railway safety near
the earthquake source fault regions.

S-wave “specified threshold exceeded” (S-wave warning)P-wave warning≥1 sec.

≤1 sec.

Warning issued in≤ 1 sec.

P-wave detection

P-wave “specified threshold exceeded” earthquake warning (P-wave amplitude x S/P-wave amplitude ratio)

MYZH05

KMMH16

KMMH09KMMH07

KMMH06

KMMH03

MYZ020

MGS012

Mean±σKMM018KMM013

KMM008Mean

KMM006

KMM005

Individual earthquakes

KMM00310-1FK0016

10

0

Seismic observation stations

Fig. 2 S/P-wave amplitude ratio at seismic observation stations

4

Time taken to issue warning (s)101

KMM002

S/P-wave acceleration amplitude ratio

Fig. 1 P-wave “specified threshold exceeded” earthquake warning method

4

Faster than current
method by 1 sec. or more

Near(within the fault region)
Outside3
2
1
ProposedmethodP-wavewarning

S-wave warning

Fig. 3 Time taken to issue warning

Annual Report 2021-2022

Railway Technical Research Institute

2. Design method for bearing parts to control damage and improve restorability• We proposed a design method for bearing parts that increases the precision of calculations of thestrength of parts with embedded stoppers and limits damage to parts that are difficult to restore after
an earthquake.
• The new method reduces construction costs of hard-to-restore damaged parts after an earthquakeby 80%.
Stoppers are embedded in the bearings of concrete bridges
to prevent bridge collapse.
Damage to bearing parts with embedded stoppers wasobserved after many recent earthquakes in Japan, evenwhen there was no collapse or safety threat (Fig. 1). In
such cases, when damage occurs as a result of concrete
peeling at the area shown in Fig. 1 (2) (girder end), whichis narrower than shown in Fig. 1 (1) (pier front), repairing
the bridge is difficult. Therefore, controlling the damage tothese difficult-to-restore areas is an effective way to shorten
the time and cost of restoration work after an earthquake. In
light of this, we proposed a method of calculating strengththat is capable of assessing the influence of bending shapeand configuration of rebar to improve the strength, without
increasing the quantity of rebar in parts with embedded
stoppers (Fig. 2). With the conventional method, the same
strength is calculated regardless of rebar bending shapeand configuration, despite the fact that loading tests andFEM analysis show that strength varies according to thesefactors. We therefore proposed a strength calculation
method that takes into account the impact of rebar bending
shape and configuration.
With the conventional design, damage may occur at thegirder ends, which are difficult to repair, but we confirmedthat the new method limits the damage to such hard-torestore parts of a bridge to a minimal level, while at thesame time ensuring equal or better strength than theconventional method (Fig. 3). By eliminating the need touse heavy machinery for chipping concrete and erecting
scaffolding, which are essential for repairing earthquakedamaged parts of a bridge, construction costs can be cut
by 80%.
These findings will be reflected in the upcoming revisionof the Design Standards for Railway Structures andCommentary (Concrete Structures).

Part with embedded stopper
Girder

lrh

Rebar

lrd
Stopper

Pier
Stopper

1400
Damagedpart(1)
Narrow

Inertia force

GirderHard to (2)repair(1) Easy torepairPier
Dimensions needed to prevent collapse

Fig. 1 Example of earthquake
damageAnnual Report 2021-2022

1000

Pier
Conventional
calculation method

800
600

Proposed calculation
method400

StressInertia force
High
Low
GirderDeformation factor x70
Stopper[Designed using
proposed method]

Strength: 759 kN
Rebar shape change

Minor damage at (2)2000Strength: 691 kN

Fracture at (2) is hard to repair
At max.
load

Analysis value

1200
Strength (kN)(2)

[Designed using
conventional method]

Low
Girder

0

1.0

2.0

Coefficient expressing rebar shape√ lrd /lrh

Fig. 2 Shape and strength of rebar inparts with embedded stoppers

Pier

Fracture at (1) iseasy to repair

Fig. 3 Comparison of damage in parts
with embedded stoppers

5

Major Results of Research and Development

3. Method for verifying the restorability of railway structures considering small- and medium-scale earthquakes• We proposed a method for verifying the restorability of railway structures considering small- andmedium-scale earthquakes as well as large-scale earthquakes (Level 2 seismic motion)• With the method, the number of days needed for restoration after an earthquake is used as a verificationindex in the design of new structures and the assessment of existing structures, making it possible
to construct structures that are easier to restore.
Current seismic design of railway structures requires thatdamage resulting from a large-scale earthquake of seismic
intensity 7 (Level 2 seismic motion) is rapidly repairable.
However, the structural performance against mediumscale earthquakes of seismic intensities “6 lower” and “6upper,” which are occurring frequently in recent years, is
not clearly defined. As a result, some issues have emergedin relation to “rapid” restoration. For example, it took along time to resume operation after the 2018 northern
Osaka Prefecture earthquake and the 2021 northern Chiba
Prefecture earthquake.
To solve this problem, we proposed a method for verifying the restorability of structures taking into consideration
the effects of small- and medium-scale earthquakes. A
feature of the design flow of the proposed method (Fig. 1)
is that it applies to “all seismic motions including small- andmedium-scale earthquakes” and that “the number of days
required for restoration is used as a verification index.”
This method can be directly adopted into frameworks ofcurrent seismic design.
To make the design with the proposed method more practicable, a nomogram (Fig. 2) for the required strength (yield

START

seismic intensity) of structures was developed for each of
required restoration time, conditions (e.g., region, structural type), and damaged structural parts (e.g., foundation,column). The results show that to ensure the same levelof restorability as a structure with prior column damage, astructure with prior foundation damage requires a higher
yield seismic intensity. In past earthquakes, it takes manydays to restore a structure whose foundation has beendamaged. This trend is in accordance with the results in
Fig. 2. Fig. 2 shows the yield seismic intensity necessary
to make a structure restorable in five days, but it is possible
to design in the same procedure as usual by selecting thenomogram corresponding to the required number of days
for restoration.
The proposed method makes it possible to design new
structures that are easier to restore after earthquake damage. Moreover this method can identify the parts of existing
structures that require most time for restoration, so it ispossible to shorten the time until operation resumes after
an earthquake by applying focused proactive measures
to these parts.

No. of restoration days

Set required performance and verification index
All earthquakes expected atconstruction place(incl. small and medium-scale
earthquakes)

Set cross-section specs., etc.
Calculate structural response value
Evaluate no. of restoration days
Is performance verified?

YES

Take into account small andmedium-scale earthquakes

NO

END
Fig. 1 Design flow with proposed method

6Case of rigid frame viaduct
Required yield seismic intensity

Set design earthquake motion

Prior yield place:foundation1
0.5
Prior yieldplace: column

0.1
0.1

0.5

1

5

Natural period of structure (sec.)

Fig. 2 Nomogram for restorability verification(example of five days for restoration)

Annual Report 2021-2022

Railway Technical Research Institute

4. Method for evaluating vehicle safety in strong winds using
probabilistic risk assessment• We proposed a method to evaluate the safety of vehicles based on the probability of external wind force
exceeding the overturning resistance of vehicles to strong winds by expressing both the overturning
resistance of vehicles to strong winds and external wind force as a probability distribution.
• The method can be used to set wind speed values for operation control in accordance with criticalwind speeds of overturning for vehicles and actual wind conditions.

Distribution of max. wind
Measured speed in strong wind
value
events (strong winds
observed to be over thewind speed for operation
control)

0.2
0.1
0
20

0.2 Cumulative

Probability density function

25

30

35

40

45

0.1

50

1

distribution function
Probability
density function

Distribution of criticalwind speed ofoverturning, determinedfrom measured values oflateral acceleration.

Measured
value

0
29

30

31

32

33

Probability

0.3

of overturning and wind speeds in windy conditions, each isexpressed as a probability distribution (Fig. 1). By applying
the concept of failure probability, the probability of a windspeed exceeding the critical wind speed of overturning
is determined and used as a risk index Pf to express thepossibility of the vehicle overturning (Fig. 2).
This risk index can be used to determine appropriate windspeed for operation control for the type of vehicle and windconditions along the railway line. For example, a safe windspeed for operation control can be specified based on therisk index value determined under conditions where no
accidents have occurred in the past. When a new type of
vehicle with a different critical wind speed of overturning
is deployed, the wind speed for operation control can be
flexibly modified to match the different critical wind speedof overturning using a uniform risk index for that sectionof railway line (Fig. 3).

Relative frequency
probability density

Relative frequency
probability density

In recent years, as meteorological disasters have become
increasingly severe, making railway services more resilient
against strong wind disasters has become an urgent challenge. At the same time, tougher regulations relating tostrong winds can lead to less stable and convenient transportation. In light of this, we introduced a probabilistic risk
assessment method to the safety evaluation of vehicles
operating under strong wind conditions and proposed asafety assessment method for preventing trains from overturning and for maintaining stable transportation services.
The lateral acceleration used for calculating the criticalwind speed of overturning, which represents the overturning resistance of a vehicle to strong wind, is not constant;it varies depending on travel conditions. The wind speed
values of the natural wind, which represents the external
force that overturns the vehicle, also have variability. To
assess the effects of the variation in the critical wind speed

0.5
0Critical wind speed of overturning (m/s)

Wind speed along railway line (when wind speed for operation control is 25 m/s) (m/s)

Fig. 1 Example of a probability distribution for wind speed along the railway line and critical wind speed of overturning

Probability density
function of wind speed
along railway line

0.1

Risk index Pf(this area)

25

30

35

40

0.5

0

Risk index Pf

0.2

Cumulative
distribution functionof critical wind speedof overturning

0
20

2.0

1
Probability

Probability density

0.3Wind speed for operation control 27 m/s
Wind speed for operation control 26 m/s

1.0
Wind speed for operation control 25 m/s* Normalized such that Pf=1 at wind speed for operation control 25 m/s
and critical wind speed of overturning 30.8 m/s

0.0
30.8

31.8

32.8

33.8

Wind speed along railway line, critical wind speed of overturning (m/s)

Critical wind speed of overturning when lateral acceleration is 0 m/s2 (m/s)

Fig. 2 Risk index Pf based on the concept of failure probability(for wind speed for operation control of 25 m/s)

Fig. 3 Example of Pf comparison based on differences inwind speed for operation control and critical wind speed ofoverturning

Annual Report 2021-2022

7

Major Results of Research and Development

5. Inspection of gaps by hammering slab track filling layers andprediction of deterioration• We developed a method for detecting gaps between track slabs and filling layers by combining
hammering tests and machine learning, as well as a test device capable of inspecting one track slab
every five minutes.
• We also developed a method for predicting strength loss in filling layers and gaps between trackslabs and filling layers due to frost damage and other causes, by means of a coupling analysis of heat
conduction and structure.
In cold regions, frost damage tends to reduce the compressive strength of the filling layers of slab tracks. Train loads
can also cause gaps between the track slab and filling
layer. To quantitatively and efficiently detect the extent ofsuch gaps, we proposed a method of detection that combines a hammering test and machine learning. Applicationof this method to slab tracks confirmed that gaps can bedetected with 90% accuracy (Fig. 1). To inspect a large
number of track slabs in a short time, we developed acontinuous hammering test device with 11 hammering units(Fig. 2). This device makes it possible to inspect a single
track slab in approximately five minutes.
When intensive repairs are conducted to extend theservice life of slab track in cold regions, it is necessary to
predict deterioration of filling layers in order to select whichareas of the laid slab track will require repairs. ThroughFor machinelearning

experiments, we confirmed that the compressive strengthof filling layers decreases with the number of freezing andthawing cycles and that the amount of plastic deformation
due to repeated loading increases as the ratio of compressive stress to compressive strength increases. Througha coupling of unsteady heat conduction analysis andstructural analysis, we identified a mechanism by which
the compressive stress and the amount of plastic deformation at the outside of a filling layer increases when a
track slab is subjected to train loads under upward convex
warping caused by rising temperatures during the day (Fig.
3). Based on these findings, we developed a method forpredicting deterioration capable of determining the loss ofstrength in a filling layer and the gap between track slab
and filling layer (Fig. 4).

PC for control andrecording

Determination
results
No gapWith gap

Range of produced gap

Soundcollector

Rail fastening system
Example of determination
results with hammering
test utilizing machinelearning

Fig. 1 Determining gaps using hammering test andmachine learning(1) Transient heat conduction analysis(2) Calculate compressive strength

Calculate minimum
temperature of fillinglayer

Calculate compressive strengthfrom minimum temperature andno. of freezing and thawing
cycles(4) Calculate gap
Calculate gap fromcompressive stress/
compressive strength andnumber of trains(3) Coupled analysis of heat
conduction and structure Train load
Calculate compressive
stress of filling layer
caused by warping of
track slab

Hightemperature

Low temperature

Loop from (1) to (4) expands a gap

Fig. 3 Flow of deterioration prediction

8

Hammeringunit

Fig. 2 Continuous hammering test device

0

1

2

3

4

5

N/mm2

0

mm

0.1 0.2 0.3 0.4 0.5

40 years
service
When trackslab cracks(80 years
service)Compressive
strength drops

Gap expands

Compressive strength of filling layer

Gap sizeFig. 4 Example of predicting filling layer deterioration

Annual Report 2021-2022

Railway Technical Research Institute

6. Method for estimating snow accretion and snow droppingof a train vehicle for investigating countermeasures againstsnow dropping• The amount of snow that accumulates on a bogie and the locations where snow drops off from it canbe estimated in real time from weather information along the line.
• The growth of snow accretion on the cover plate of a bogie can be estimated within an error of
approximately 3 cm, and sections of line where snow dropping occurs frequently can be estimatedwithin an error of approximately 2 km.
• This method can be used to determine whether snow removal work is necessary and to examine
sections where measures against snow dropping are needed.
In snowy areas, snow thrown up by moving vehicles cansettle on cover plates of bogies and the accumulated snow
can drop off while the train is moving, possibly damaging
ground equipment. To efficiently and effectively implement
measures against snow accretion and dropping, we developed a method for estimating the snow accretion and snow
dropping of a train vehicle. This method can be used toestimate the amount of accumulated snow and the location
where snow drops off in real time, based on information
about train operation and weather along the line.
Previous methods did not take into account snow dropping except in long tunnels. This method can calculate theamount of snow accretion on the cover plate of a bogie inopen sections using the snow accretion rate determined inadvance by an already developed snow accretion simulator, based on information about weather along the line. In

addition, it can take into account the loss of snow whensnow drops off in warmer environments such as tunnels.
The method is therefore capable of estimating the rate ofsnow accretion in real time (Fig. 1).
We confirmed that this method is able to estimate thesnow accretion rate on arrival at a station with an error of
approximately 3 cm (Fig. 2) and to estimate the sections
in tunnels where snow drops off frequently with an errorof approximately 2 km (Fig. 3).
The estimated amount of snow accretion on arrival at a
station can be used to determine whether snow removal
work is necessary or not. Estimation of sections where
snow drops off frequently can be useful in planning equipment inspections and prioritizing countermeasures againstsnow dropping.

Station A

Station B

Estimated snow accretion (mm)300Long tunnel

Train travel direction

250
200

Measuredvalue

Proposedmethod150

ProposedmethodPrevious method

100

Snow dropping occurs

50
0

0

10

20

30

40

50

60

70
80
Distance (km)

90

100

110

120

130

140

150

Fig. 1 Example of calculation of method for estimating snow accretion and dropping of a train(the gray shaded areas are tunnel sections)

200

50

Measured value

Estimated value

Frequency (%)

Snow accretion (mm)

250

150
100
50
7 Dec.

30

11 Dec.

15 Dec.

19 Dec.

Date

23 Dec.

27 Dec.

31 Dec.

Fig. 2 Comparison of estimated and measured
values of snow accretion rate on arrival at Station B

Annual Report 2021-2022

Measured value

20
10
0

0

N=84

Estimated value

40

0

5

10

15

20

25

30

35

40

45

50

Distance from tunnel entrance (km)Fig. 3 Comparison of snow dropping locations ina long tunnel (frequency at intervals of 2.5 km)

9Major Results of Research and Development

7. Method of applying image processing-based object detection and obstacle determination to safety applications• We developed a method of self-diagnosing camera and image processing unit failures.
• Applying this method to an abnormality detection equipment at level crossings, we confirmed that it iscapable of fault diagnosing at 0.1-second intervals.
• This method enables the implementation of image processing-based object detection and obstacle
determination functions in safety-related equipment.
Since image processing units that use cameras andgeneral-purpose processors cannot diagnose failures
by themselves, they cannot ensure sufficient safety inthe event of a failure. Therefore, they cannot be used insafety-related equipment. One way to address this issue ofimage processing unit failures might be to perform image
processing using a current signalling computer which is
specially designed for safety-related applications (fail-safe
components). Due to slow processing speed, however, thisis not a practicable solution. And since image processing
uses machine learning and random numbers, conventional
diagnostic methods that utilize multiple image processing
units to check for bit-perfect matches of processed datacannot be used either.
To solve this problem, we developed a method that works
in combination with a fail-safe component to perform
high-speed self-diagnosis of failures in image processing
units and cameras (Fig. 1). The method diagnoses image
processing errors in image processing units by focusing
on the similarity of processing results. Specifically, theprocessing contents of the image processing unit arecompressed in the diagnostic image and the similarities arethen determined in the fail-safe component as “differencesof the diagnostic image = similarities.” In addition, by adding a function to the camera that embeds diagnostic datain the image data and outputs a test pattern, we proposeda method to diagnose image freezing and other issues
using a fail-safe component (Table 1).
We trial manufactured a detection equipment that implements the proposed method, to serve as an abnormality
detection equipment at level crossings. By means of an
artificial fault test, we verified that the method works andis capable of fault diagnosing the operational status of theequipment at 0.1-second intervals.
The proposed method can detect large differences that
influence determinations, while still allowing differencesin processing results in multiple image processing units
arising from machine learning (e.g., background images
updated every cycle). It can therefore be applied as a failsafe configuration in AI and general-purpose processorsthat handle non-image data. This approach to camera
diagnosis can also be applied to other general-purpose
sensors for LiDAR, millimeter wave radar, etc.

Table 1 Failure modes and diagnostic methods

CameraImage processing
unit B

Detection process

Fig. 1 Configuration and diagnostic procedure

10

Diagnostic method

Machine learning processing error
Image processing error

Comparison ofcompressed image
for diagnosis

Object detection error

Output comparison

Image is frozen; it is not
updatedIntegrity
diagnosis
Compressed
image fordiagnosis

Failure mode

Image dropped frames
Image capture cycle shift/error

Diagnostic data

Fail-safe component

Compressed
image fordiagnosis

DeviceImage
processingunit

Add diagnostic
data to image
data

Detection process

CameraImage processing
unit A

Inspection
codes
Frame countClock countDiagnostic data error

Test pattern

Pixel failure (blockedshadows, blown highlights)

Level analysis

Annual Report 2021-2022

Railway Technical Research Institute

8. Support system for preventing a decline in alertness level ofdrivers• We developed a system that detects the alertness level of drivers with images and issues an alarm.
• It can estimate alertness level with an accuracy of 80% or higher, even when the driver is wearing amask.
• With the proposed alarm presentation method, 90% of people with reduced alertness level reported
that they felt “awakened.”
As part of the efforts of railway operators to ensure safe
and stable transport services, preventing a decline in thealertness level of train drivers is an important challenge.
While methods for estimating the alertness level of drivers
have been implemented in automobiles, in the case of
trains, there is an issue with ensuring accuracy due tothe wide variety of driver behaviors. This study attemptedto address this problem by developing a system that estimates a driver’s alertness level based solely on informationfrom images of the driver’s face while driving and issues analarm sound when the alertness level drops by a specified
amount (Fig. 1, left).
The system captures facial images using a fixed camera in
the train, without any sensors contacting the driver’s body.
Deep learning is used to evaluate alertness on a scale ofsix levels. In an experiment with 112 general test subjects,
we confirmed that the system is capable of accurately
gaging the alertness with an estimation accuracy of 80%
or higher, even when masks are worn (Fig. 1, right).

For the alarm that is issued when a drop in alertness levelis detected, we developed a sound that is highly rated for
its functionality (strong arousing effect), perceptibility (easyto hear), and its distinguishability (not mistaken for othersounds). When test subjects in a laboratory experiment
suffered a drop in alertness, their reaction time in a simple
reaction task recovered when exposed to this alarm sound(Fig. 2), with approximately 90% of subjects responding “Ifelt awakened” in a questionnaire.
After the system was trialed on a commercial train, theresults confirmed that the system accurately captures
facial images, even under diverse lighting conditions, andthat it is capable of estimating the alertness level of drivers
in real time. Three drivers (including a conductor) rated thearousal effect of the alarm sound, giving it a score of 5 ormore out of 6 points (Fig. 3).

Simple reaction task

Near IR camera

Light environment
control
Face detection
Feature point/qty.
extraction

Mask detection
Estimate alertness
level

Estimated alertness
level

900
800

IR illumination

600

Before
sound

After
sound

Fig. 2 Arousal effect of alarm sound in a laboratory experiment
Face detection
React to faceangle, etc.

Estimatealertness level
React to mask

Alertness level accuracy rate

80.1%

Fig. 1 Configuration of support system for preventing adecline in alertness level

Annual Report 2021-2022

1100
1000

700

React to diverse
light environment
in driver’s cab

Mask ON
Alertness Level 3
Alarm display
deviceReaction time (ms)Camera[System]

Recoverreaction time

1200

Arousaleffect1
I couldn’t stayawake at all.

2

3

4

I couldn’t stay I could barely I was somewhat
awake.
stay awake.
awake.

5

6

I wasawake.

I was very
awake.

Fig. 3 Arousal effect of alarm sound in an on-track test11

Major Results of Research and Development

9. Phased array ultrasonic testing method for easy detection offlaws in bogie frame welds• We formulated an ultrasonic testing method and procedure for bogie frames based on the phasedarray method that can easily detect flaws in welds.
• The method easily distinguishes between flaw signals and noise, and effectively detects inclined flaws
and flaws in coatings.
• Detection sensitivity is equal to or better than that of conventional ultrasonic testing methods.
Since the bogie frames of railway vehicles are fabricatedwith numerous welds, any flaws within welds can poserisks of serious damage. Conventional inspection methods
require a high level of skill to detect flaws, however. In
light of this, we formulated a method and procedure fordetecting flaws in bogie frames based on the phased arrayultrasonic testing (PAUT) method. The method is capableof easily detecting any flaws inside welds.
Unlike conventional ultrasonic testing methods, whichtransmit and receive ultrasonic waves at a fixed angle(conventional UT method), the PAUT method transmits andreceives waves at various angles to obtain cross-sectional
images that enable visualization of flaw detection results(Fig. 1). Since images are used to discern flaws, distinguishing flaw signals from noise due to the shape of welds
is easy, greatly reducing the chances of missing flaws.
And since ultrasonic waves can be directed at flaws fromvarious angles, this method is also effective in detecting

Conventional ultrasonic testing method(conventional UT method)
Scan probe
Fixed angle

Phased array ultrasonic testing method(PAUT method)
Dozens of oscillators

Electronic
swing-scanningof ultrasonicwaves

Bogie frame ultrasonic testing model

Weld
Inclination
angle, η

Expandedview of flaw

Bogie partArraytransducer

Bogie part

PAUT
probe

Side beam

Weld

Swing-scanning of
ultrasonic waves

Flaw

Flaw signal amplitude (echo height) (%)

Singleoscillator

inclined flaws with an inclination of 30° or more, which
are typically difficult to detect using the conventional UT
method (Fig. 2). Although detecting flaws in bogie frames isusually performed after removing the coating, our analysis
has shown that even when this inspection method is applied without removing the coating, detection sensitivity
is reduced by no more than 20% for coating thicknesses
up to 1 mm.
With the conventional UT method, the flaw detection
procedure is based on the Japanese Industrial Standard(JIS) “Method for ultrasonic testing for welds of ferritic
steel.” In the newly developed PAUT method procedure,a sensitivity adjustment procedure based on the same
standard flaw as used in the JIS method, enables a flaw
detection sensitivity equal to or better than the conventional
UT method. Since flaw detection results with the new PAUT
method are comparable to those of the conventional UT
method, transition to the new method is easy.

Bogie frame(side beam)Internal reinforcement

500

PAUT method

400
300
200

Conventional
UT method

100Standard flaw echo height

0
-60

-45

-30

-15

0

15

30

45

60

Flaw inclination angle, η (°)

Fig. 1 Outline of phased array ultrasonic testing method (PAUT method)

12

Fig. 2 Comparison of detection sensitivity for inclined flaws between
conventional UT and PAUT methods

Annual Report 2021-2022

Railway Technical Research Institute

10. Oil for axle-box for Shinkansen with excellent low-temperature performance and maintainability• With the expansion of the Shinkansen network into extremely cold regions of Japan, we developed alubricating oil for axle journal bearings of Shinkansen train that retains its fluidity even at temperatures
below -40°C and that prevents reddening over time.
• The lubricant has been confirmed to have excellent durability in a bench test on actual axle journalbearings for the equivalent of 800,000 km of travel.
Currently, only one brand of oil is used to lubricate the axle
journal bearings of Shinkansen trains. However, there wereconcerns that this oil would not cope when the Shinkansen
network was expanded into extremely cold regions, due toa lack of low-temperature performance. The lubricating oil
developed by the RTRI in the past (“Axlube”), made usingsynthetic oil for enhanced low-temperature performance,
has not been adopted because it turns red over time,making it difficult to distinguish this color change fromthe presence of abrasion debris, which is a problem for
maintenance. To solve these problems, we developed a
new oil for axle-box for Shinkansen (developed oil) thatoffers both excellent low-temperature performance andmaintainability.
The developed oil utilizes a highly refined mineral oil asa base oil and includes additive substances that inhibit
reddening. The developed oil has a lower pour point thanthe current oil and is verified to retain its fluidity even at
temperatures below -40°C (Fig. 1).

Outdoor exposure tests have also confirmed that thedeveloped oil is capable of inhibiting reddening causedby using Axlube (Fig. 2).
We verified the durability of the developed oil by conducting a bench test with real axle journal bearings operating
for the equivalent of 800,000 km of travel. At the end of
testing under these conditions, no abnormalities were
found in the properties of the oil or the test bearings,
confirming the oil’s outstanding durability (Fig. 3). Thebase oil and additives that make up the developed oil are
general-purpose products that are employed widely in oils
for other applications. This helps to keep costs down andensure a stable supply of raw materials.
The developed oil is expected to be deployed on
Shinkansen vehicles that use oil bath lubrication after its
durability is confirmed on operational vehicles.

Developed oil-20

Developed oil

Pour point (°C)-10

Current oil

0

ReddeningoccursAxlubeNo reddening

Fig. 2 Results of outdoor exposure test

Outer ring track surface

Inner ring track surface

-30

-40

Improved
low-temperature
performance

RollerNo abnormality in test
bearing-50
Fig. 1 Pour point* of current oil and developed oil* The lowest temperature at which the oil flows

Annual Report 2021-2022

Fig. 3 Test bearing after bench test (equiv. of 800,000 km)

13Major Results of Research and Development

11. Seat surfaces for minimizing injuries to passengers in theevent of a collision• We developed a seat surface for walkover seat that minimize injuries to passengers in the event of acollision.
• To ensure comfort, a cushioning structure is incorporated into the seat surface. No major designchanges to current seats are required, since the cushioning effect is achieved by the seat surface alone.
• In the event of a railway crossing accident, the new seat surface can reduce lower limb injuries inpassengers by 60% compared to the previous one.
When passengers are seated on seats arranged laterally(in the direction of sleepers), e.g., on the walkover seat
fitted in suburban vehicles, they can suffer lower limb
injuries if they are thrown forward into the seat in front ofthem by the impact of a train collision. This risk has beenhighlighted in past railway accident investigation reports. In
Europe and the U.S., there are regulations aimed at limiting the severity of injuries, and in some cases seats areeven fixed in a non-convertible structure to help minimize
injury severity. Up to now in Japan, however, the severityof injuries has not been assessed in this context. In lightof this, we developed a seat surface capable of reducing
the severity of lower limb injury in the event of a collision.
In the seat surface structure of a conventional seat, thecushioning is typically supported from below by a top plate
surrounded by highly rigid steel pipe (Fig. 1). This structure
is a factor that tends to worsen severity of injuries that
occur when a passenger’s lower limbs collide with the seatin front of them. Therefore, after subjectively evaluating thestructure to ensure that there is no loss of sitting comfort,
we devised a cushioning structure such that the part
where a passenger’s lower limbs would impact is madeof aluminum angle and supported by urethane at both ends
(Fig. 2). To confirm the effectiveness of this structure in
reducing injuries, we fabricated a prototype seat surface
and conducted a sled test under a hypothetical level crossing accident (Fig. 3). Our findings confirmed that the newseat reduced the severity of lower limb injury by about 60%
compared to conventional seating surfaces. This is evenlower than the injury severity limit stipulated in the technicalreport of the European Rail Supply Industry Association(UNIFE), which is widely referenced for evaluations in
Europe and other countries (Fig. 4).
Since a substantial shock-absorbing effect can be achieved
with only the seat surface, when railway operators develop
seats aimed at mitigating injury risks in the event of acollision, this new design can be introduced without any
major change to existing seat designs.

CushionSubjective
evaluation:
Review structure
to ensure no lossof sitting comfort
Aluminum
angle barSeat surface

Copper tube

Urethane

Mock-up seat surface

Top plateFig. 1 Seat surface structure of conventional seats

Fig. 2 Seat surface with embedded cushioning structure forsitting comfort

Seat surface is
fixed

Coll

isio

n sc

ena

rio a

t lev

el c

ross

Lower limb
collision

ing

Fig. 3 Confirming effectiveness in a sled test

14

Severity of lower limb injury

Dummy

4.0
3.0
2.0

UNIFE limit value

1.0
0.0

Conventional seatsurfaceCushioning structure
seat surface

Fig. 4 Effectiveness in reducing lower limb injury

Annual Report 2021-2022

Railway Technical Research Institute

COST REDUCTION

12. System for determining the degree of deterioration ofwooden sleepers using images of railway tracks in frontof trains• We developed a low-cost system for determining the degree of deterioration of wooden sleepers
based on a deep learning technique that uses images of the view in front of a train captured with an
off-the-shelf video camera.
• The system can automatically judge the degree of wooden sleeper deterioration, according to afour-level classification scheme, with an accuracy of 90% or higher.
• Since the system displays the rated degree of deterioration directly on images, defective sleepers canbe visualized easily using a viewer.
On sections of railway lines constructed mainly of wooden
sleepers, there is a risk of “derailment by gauge widening”
due to inadequately fastened rails caused by the decay ofwooden sleepers. For this reason, the number of defective
wooden sleepers and the state of individual sleepers needs
to be continuously controlled, particularly along curving
track. However, visual inspections of huge numbers of
sleepers requires enormous labor. To solve this problem,
we developed a system that uses video captured with
an off-the-shelf video camera to automatically detect andjudge the degree of deterioration of wooden sleepers byapplying image processing and deep learning techniques
to a simulated under-floor image, which is a projective
transformation of images of railway track in front of the
train (Fig. 1). This system can be installed on any train inless than ten minutes, by just setting up a video camera. It
is also less expensive than the under-floor image capturesystems currently used by JR companies.
When we applied this system to images of approximately

16,000 wooden sleepers taken from above a train on acommercial line, we confirmed a sleeper detection rate of
99.5% (100% except when the sleeper surface is coveredby a significant amount of ballast or other material). A
comparison of the degree of deterioration determined bythis system and that determined from images (4 levels)by a track maintenance engineer also confirmed that thesystem is capable of automatically assessing deterioration
with an accuracy of 90% or more (Table 1). In this case,the rate at which bad sleepers (A2, B) were mistaken forgood sleepers (C, D) was 7%, but the probability of missing
successive defects, which increases the risk of derailment
by gauge widening, was considered sufficiently low. With
this system, each sleeper is output in a color-coded format
to denote the degree of deterioration, both to a viewer that
displays the results and to a tabular inspection record book,making it easy to identify areas where there are successive
defects in wooden sleepers.

Capturing images of railway track in front of train to simulate under-floor images
Rail

Captured images
WoodensleeperVideo camera

Generate simulated under-floor images

Using judgment results for wooden sleeper inspection (visualizing defects)
ViewerOutput register (record book)
Rail

No. No.

Judgment results are color-coded

1
2
3
4
5
6
7

1
2
3
4
5
6
7

No. of km

Length(m)

001 m to 019 m 18.3
019 m to 043 m 23.7
043 m to 066 m 23.8
066 m to 090 m 24.0
090 m to 115 m 24.3
115 m to 139 m 24.2
139 m to 163 m 24.4

Sleeper No.

1
D
B
D
C
C
D
D

2
3
4
5
6
D C B B C
D D A2 D D
A2 D A2 B D
B D D B D
D C D D A2
D A2 A2 A2 D
C D D C C

Table 1 Sleeper detection rate anddeterioration rating precision

Wooden sleeper detection rate

99.5%

Deterioration
level

Ratingcriteria

Ratingprecision

A2

Decline ingauge retentionfunction

92.2%

B

Decline insleeper function

88.6%

C

Minor damage

91.2%

D

Good

94.1%

Poor

* It is also possible to calculate spacing of sleepers and sleeper angulardeviation from right-angle position.
System is also applied to understand the conditions of rail fastening system,
track bed, etc.

Fig. 1

Restoration method of embankments using the dehydration effect oflime and the increase in the strength of soil due to compaction

Annual Report 2021-2022

Good* No. of rated wooden sleepers: 16,033

15

Major Results of Research and Development

13. A method for constructing communication-based train
control systems using public communication network• We proposed a design procedure for creating train control systems using public communication network
such as 5G and a method for integrating information security functions in safety equipment.
• The method enables a reduction of up to 50% in the total number of ground equipment that make up
the system and promises to achieve labor-saving in tasks related to facility design and maintenance.
The increasing deployment of communication-based train
control systems is helping to increase the efficiency of train
operations and reduce the need for ground equipment. In
the coming years, use of public communication network
such as 5G is expected to further reduce the number of
equipment that railway operators need to self-manage,
thereby enabling significant savings in design and maintenance work. However, in conventional systems, safety is
guaranteed by functions that rely on equipment of private
communication network that restrict information transmission. When public communication network is used, on theother hand, there is no specific method for implementing
the information security measures necessary to ensure
safety.
To address the above issue, we proposed a system
configuration that makes information transmissionfunctions, such as base stations, independent of safetyrelated security control functions of ground and on-board
equipment and formulated a procedure for designinga communication-based train control system based on
this configuration (Fig. 1). We also defined the safety
and information security requirements that such safety
equipment must meet to withstand external cyber-attacks

on the communication network by third parties. As a
specific implementation method, we proposed a method
for integrating cryptography-based security functions in
non-fail-safe modules and diagnosing the normal operationof these functions using test data from each message(Fig. 2). We also conducted a functional verification on a
prototype module using a single-board microcontroller. By
designing a system based on these methods it becomeseasy to make use of public communication network. And
by making safety equipment independent of update timing,
hardware can be replaced with the latest communications
technology and functions can be improved at any time,
without compromising security. Depending on operational
conditions and system configuration, the total number ofground equipment can be cut by around 50% by eliminating the need for base stations of private communication
network and other radio equipment.
The findings of this study were adopted as a basic configuration to future communication-based train control
systems by the “Study Group on Communication-Based
Train Control Systems” established by the Ministry ofLand, Infrastructure, Transport and Tourism and reflectedin published guidelines.

FS: Fail-safe
System design procedure(1) Define train line conditions

System configuration
Ground equipment

Transmitter
Radio equipment

Controlled
message

Define system constraints from operation
conditions and handling

Sent message(2) Determine system configuration

On-board device

On-board station

Receivedmessage
Controlled
message

Safety function

Informationtransmission functions

Make functions independent(eliminate dependency)

FS-CPU

Non-FS module

FS-CPU
Extract message
authentication code

Security measures
Non-FS module
Generate message
authentication code

Verification
Message
adoption

Base stations, antennas, etc. are
replaced by public communication
networkFig. 1 Design procedure for communication-based train control system

16

Generate message
authentication code

Public communication network(4) Define requirements and specifications
Define and specify requirements fortransmission timing and quality, etc.

Generate message
authentication code

Antenna

Design a train control system to satisfy
defined train line conditions

Decide information required for system
operation and where it needs to becommunicatedTest data
Verification

Base station(3) Determine control information

Integrity of non-FS module

Receiver

Test data

Generate message
authentication code

Verification

Fig. 2 Information security measures

Annual Report 2021-2022

Railway Technical Research Institute

14. Automatic anomaly detection using vehicle condition
monitoring data without additional sensors• We proposed a method for automatically detecting anomalies in vehicle equipment in operations
without additional sensors using data from vehicle condition monitoring devices and machine learning
driven by neural networks.
• We confirmed that the method is capable of promptly detecting actual failures occurred in operational
vehicles.
In recent years, more and more systems for recording andsummarizing the operating conditions of vehicle equipment
have been adopted, enabling the accumulation of large
quantities of vehicle condition monitoring data. This data
can be used to promptly detect anomalies in vehicle equipment in operations without additional sensors, thereby
improving the operational reliability further. However, due
to complex changes in vehicle equipment conditions, it canbe difficult to detect anomalies using only simple criteria,such as “when a threshold value is exceeded.”
We therefore proposed a method for extracting the characteristics of vehicle equipment conditions from vehicle
condition monitoring data using a neural networks-based
machine learning technique to determine the degree of
anomaly (anomaly score) of the vehicle equipment (Fig. 1).
(1) Firstly, a neural network is trained with vehicle condition monitoring data when vehicle equipment operates
in normal conditions and a model is created to estimate
item A, which expresses equipment conditions that would
be affected by anomalies, from other items. (2) Next, the(1) Training

A

Normal data
Item C
Item B
Item Amodel and data not used for training are utilized to obtainthe frequency distribution of differences between the estimated values and the actual measured values (estimation
error distribution) of item A in normal conditions. (3) Finally,
based on the estimation error of item A for the data in an
actual operation and the estimation error distribution innormal conditions, the anomaly score—defined such thatthe lower the probability that the estimation error occurs,
the higher its value—is calculated for each time period.
When we applied the proposed method to vehicle condition
monitoring data acquired from operational vehicles, weverified that it is capable of promptly detecting anomalies, such as an engine overheating or a decline in air
conditioning unit performance, based on increases inanomaly scores. By applying the proposed method to acondition monitoring system as in Fig. 2, crew and traffic
controller are able to promptly ascertain the occurrenceof anomalies in vehicle equipment. This enables them totake appropriate action, such as repairing the equipment
before a serious breakdown occurs.

Anomaly!

Item A in normal conditions is estimated.
(e.g., temperature, RPM)

Training

Value(2) Making “Criteria”

Criteria (Estimation error
distribution in normal
conditions)A

Normal data (not used in (1))
Item C
Item B
Item AZ

Estimation
error

Estimation

Probability

BC

Time

Value

BC

Time

0

Z

DB

Estimation error

Traffic
controller

Anomaly score evaluation(3) Anomaly score evaluation

A

Estimation
error i20

Estimation

Value

BC

Time

Z

0
16:00

Fig. 1 Flow of anomaly detection

Annual Report 2021-2022

Anomaly!

40
ScorelData (Normal? Abnormal?)Item C
Item B
Item A(2) Detection on
ground

18:00
Time

20:00
(1) Detection on-board

Fig. 2 Example of use

17

Major Results of Research and Development

15. An soundness evaluation index and method for dealing withballasted track that is contaminated with soil
• We proposed an index for evaluating the soundness of ballast includes with soil.
• The proposed index can be applied both to ballast that has fouling over time and ballast contaminatedwith soil due to heavy rain.
• We developed a Low-strength stabilization to limit the subsidence of ballasted track bed containing
soil. Construction costs can be reduced by 90% compared to ballast replacement.

Amount of subsidence after
300,000 cycles (mm)50

Repairmaterials
Ultra-fasthardeningcement

FI=27%(unsound)Index for evaluation of health (FI)
FI>20%High risk of rapid subsidence

40high soil content, we developed a low-strength stabilization(stabilized to a strength that allows re-tamping repair) thatmixes ultra-fast hardening cement and polymeric materials
to enable repair by tamping. We confirmed in full-scale
tests that the method is effective in limiting subsidence ofballast with an FI of 20% or more, which is likely to suffer
rapid subsidence. With this method, the ballast can evenbe re-tamped and repaired again after construction (Fig. 2).
Trial constructions using multiple tie tampers on sections
that had degraded over time and using ballast tampers
on damaged sections demonstrated the effectiveness of
subsidence control (Fig. 3). This method can reduce construction costs by 90% compared to ballast replacement.
In April 2022, we commercialized a method for evaluating
the soundness of ballast and a low-strength stabilization
We also created manuals for the methods.

30
20

New ballast

Polymericmaterials

10
0

0

10

20

30

40

50

60

Amount of subsidence (mm)Ballasted track bed that has degraded over time or that has
been contaminated with soil due to flooding and landslide
after heavy rains is more likely to subside rapidly undertrain loads. The ballast is usually replaced in this case, buthigh repair costs have been an issue. In particular, there istypically a need to resume operation through the affected
section as quickly as possible without ballast replacement,by reducing the train speed. To address this issue, we
assessed the settlement characteristics of ballasted track
bed by conducting repeated loading tests on ballast withdifferent fine particles. Using the Fouling Index FI (sumof the proportions of ballast containing particle sizes of
75 μm or less and 4.75 mm or less) as an indicator of thesoundness of the ballast, we demonstrated that the riskof rapid subsidence can be determined. The higher thevalue of FI, the greater the amount of subsidence (Fig. 1).
To control the subsidence of ballasted track bed with a30
20

Subsidence
control

New Ballast
FI=0%Re-tamping
Low-strength stabilization
method

10
0
0

10

20

30

Fig. 1 Relationship between soundness index (FI)and amount of subsidence

Fig. 2 Full-scale test of low-strength stabilization method

Longitudinal level irregularityof 10 m string (mm)No. of load cycles (10,000s)

Longitudinal level irregularityof 10 m string (mm)Index for evaluation of health (FI) (%)

Unsound FI=27%30
Constructionrange
4 months after construction
20 8 months after construction
100-10 Before construction(2 months after previous-20
Subsidence control
normal repair)
FI=26%
-30
0
10
20
30
Horizontal position (m)(a) Age-degraded section (Ballast tampers)

30
Construction range
20
4 months after construction
100-10-20 2 weeks after construction
Subsidence control
-30
0
10
20

FI=24%

Before construction(3 months after
previous normal
repair)Horizontal position (m)30(b) Damaged section (Ballast tampers)

Fig. 3 Verifying the repair effectiveness of the low-strength stabilization method

18

Annual Report 2021-2022

Railway Technical Research Institute

16. Method for reducing the frequency of maintenance on standard longitudinal sleepers at boundaries with structures• We proposed a method and standard structure for laying longitudinal sleepers at structure boundaries
and verified the effectiveness of longitudinal sleepers in reducing longitudinal level irregularity in a test
installation on a commercial line.
• We confirmed that the method reduced the progression of longitudinal level irregularity to about 20%of that with conventional sleepers, making it unnecessary to perform track maintenance three to fourtimes a year.
Due to the sudden change in track support conditions,maintenance frequency tends to be higher at boundaries
with structures, such as open channels that cross railway
tracks and transitions from embankments to girders, thanat other sections of line. The use of longitudinal sleepers isan effective way of reducing the labor required to maintainballasted track. However, since such sleepers have hardly
ever been used at structure boundaries, their effectivenessfor this purpose remained unverified. In light of this, we
conducted numerical analysis and full-scale testing to design a method of laying longitudinal sleepers at boundaries
with structures, as well as to devise standard longitudinal
sleeper structures to suit a variety of conditions (Fig. 1). At
the same time, we verified the effectiveness of longitudinal
sleepers in reducing longitudinal level irregularity in a test
installation on a commercial line (Fig. 2).
More specifically, we proposed three laying methods—laying longitudinal sleepers before and after an open channel(Method 1); laying them across an open channel (Method2); and positioning the end of longitudinal sleepers on an
abutment (Method 3)—and designed a standard longitudinal sleeper structure for each method. In this way, once
the laying method is determined, the appropriate kind of
longitudinal sleeper can be selected without the need forany special design. The results from a test installation on acommercial line using Method 1 and measurements of longitudinal level irregularity proved that the rate of increaseof longitudinal level irregularity with longitudinal sleepers isfive times lower than with conventional sleepers, thereby
making track maintenance unnecessary for 22 months(track maintenance was omitted six times in two years).
Previously, track maintenance with conventional sleepers
was necessary three to four times a year. Thus, installing
longitudinal sleepers in sections that require frequent track
maintenance makes it possible to significantly reduce track
maintenance costs.
Abutment

Longitudinal
sleeperOpen channelLongitudinal sleeper■ Method (1): Laying before/after an open channel

GirderAbutment

Open channelLongitudinalLongitudinal
sleepersleeper■ Method (2): Laying across an open channel

Embankment■ Method (3): Positioning the end of longitudinal sleepers on an abutment

Fig. 1 Method of laying longitudinal sleepers at boundaries with structures
Longitudinal sleeper

Open channel
Laying conditions on commercial line
Longitudinal sleeper

Longitudinal sleeper

Open channelLongitudinal level irregularity (mm)Longitudinal sleeper* Longitudinal sleepers: Track maintenance 4 times after ballast renewal
Conventional sleepers: Track maintenance 6 times after ballast renewal

0β: Coefficient expressing the rate of increase of
longitudinal level irregularity-2

Longitudinal sleeper
β1=0.00198-4
Conventional sleeper
β 2=0.0099

-6
-8
-10

β22
β

-12
-14

Comparison of
longitudinal levelirregularity

0

100

β11
β

* β1 is reduced to 1/5 of β 2

185 days

200

300

680 days

400

500

600

700

800

No. of days elapsed (days)

Fig. 2 Effectiveness of longitudinal sleepers in controlling longitudinal level irregularity(Method (1), laying before/after an open channel)

Annual Report 2021-2022

19

Major Results of Research and Development

17. Method for evaluating the effect of impact load on fatigue
of steel bridges at rail joints• The high-frequency vibration of members caused by the impact load of wheels passing over a rail joint
contributes significantly to the initiation of fatigue cracks in steel bridges.
• We developed an analytical method for evaluating cumulative fatigue that takes into account highfrequency vibration.
• The method makes it possible to identify areas of high accumulated fatigue, thereby increasing theefficiency of inspection and reinforcement.

Longitudinal rib
Inside box girder

Fatigue crack

Longitudinal
rib-transverse
rib joint

Fig. 1 Fatigue cracks in the vicinity of rail joints

Total

30

81% of total

20
10

Out-of-plane vibration oftransverse rib
Displacement(Dominant frequency: 350 Hz) (/Max. displacement)
1.0

High-frequency vibration

Transverse rib

40

Low-frequency vibration

Steel bridge (box girder)and evaluates the accumulated fatigue in the members ofsteel bridges. A feature of this method is that the impact
load at rail joints is calculated from measured stress at one
or two locations by inverse analysis and then the stressat any location is estimated, using the impact load as an
input (Fig. 3). The method can be used to identify areas
where accumulated fatigue is large (Fig. 4), to define theareas that require concentrated attention during inspections, thereby increasing inspection efficiency. Since themethod identifies the propagation paths of high-frequency
vibration and vibration characteristics, it can also be usefulin selecting more effective reinforcement methods andenabling more efficient maintenance and management ofsteel bridges.

Ratio of fatigue accumulated at rail joints(seamless section = 1)

Fatigue cracks tend to occur frequently in the vicinity of rail
joints on steel bridges (Fig. 1). The low-frequency vibrations arising from wheel movement are known to cause
fatigue cracks. However, at rail joints, this is worsened by
the impact loads generated when train wheels roll over thejoint, causing high-frequency vibrations of several hundred
Hz in steel bridge members. Measurements on existingbridges and analysis have proven that stresses caused bythese high-frequency vibrations contribute significantly tothe initiation of fatigue cracks (Fig. 2). Since these highfrequency vibrations are localized, it is necessary to checkevery bridge member for the presence of fatigue cracks
during inspections; a significant burden.
In light of this, we developed an analytical method that estimates the stresses caused by high-frequency vibrations

0.75
0.5
0.25

0

0

Fig. 2 Contribution of high-frequency vibrations to fatigue[When rail joint is directly above a transverse rib]

Trans. rib
Impact at rail joints

Inverseanalysis

Estimated stressMeasured stress(for comparison)

Measuredstress80

A
Estimate stress at different
locations

Stress (N/mm 2)

Trans. rib

Trans. rib

Trans. rib
Long. rib
Long. rib
Long. rib

Rail joint

40[When a rail joint is 480 mm from transverse rib]

20

Long. rib0-20
0.0 0.2 0.4 0.6 0.8 1.0
Time (sec.)

Stress waveform at point A

Fig. 3 Estimating stresses at different locations using
measured stress

20

Trans. rib

Rail
joint

60
Direct analysis

Trans. rib

Long. rib

Long. rib

: Magnitude of cumulative
fatigue damage[dimensionless]: Range of significant
damage to rail joint

Long. rib

Rail joint

Fig. 4 Results of evaluating accumulated fatigue

Annual Report 2021-2022

Railway Technical Research Institute

HARMONY WITH THE ENVIRONMENT

18. Efficient method for adjusting openings in large tunnel entrance hoods• We proposed an efficient method for uniformly adjusting the area of windows arranged consecutively
in a tunnel entrance hood.
• Compared to the conventional method of adjusting the position of openings, the new method makes it
possible to reduce micro-pressure waves by the same degree, using 80% fewer trials for adjustment.
To fully demonstrate the performance of tunnel entrance
hoods as a measure for reducing micro-pressure waves
in railway tunnels, it is important to optimize the shape,
position, and size of openings arranged along the sides ofthe structure. As Shinkansen trains continue to get faster,tunnel entrance hoods are getting longer and including
more “openings.” It is therefore becoming more difficult
to optimize the conditions of the openings (open/closed)to achieve maximum effectiveness. In this study, we proposed an optimization method for uniformly adjusting thedegree of opening (height or width) of all windows (Fig.
1), as opposed to the conventional approach in which thewindows are either fully open or closed and optimization is
performed by deciding which windows to open and close(Fig. 1) The new method of adjusting the degree of opening
can reduce the magnitude of micro-pressure waves, which

depends on the maximum pressure gradient in the tunnel,
by the same degree as the conventional method (Fig. 2).
Additionally, the new method can reduce the number oftrials required for optimization by about 80% for long tunnel
entrance hoods of 30 m or more (Fig. 3).
When only a limited number of trials can be performed
to optimize actual tunnel entrance hood openings, it isessential to make the structure longer than necessary
when using the conventional method, because it is notpossible to achieve the full performance potential of thetunnel entrance hood. This new method can help to reduce
the length of tunnel entrance hoods at the design stage
because it is possible to achieve the maximum micropressure wave reduction effect (or close to it) even with
only a small number of trials.

Train(a) Adjust position (conventional method)

Train
Height(b) Adjust height opening degree (proposed method)

Fig. 1 Opening adjustment method

Adjust opening degree (proposed)

0

Adjust/optimize position (conventional)

0.0

0.2

0.4

0.6

0.8

1.0

Opening degree

Fig. 2 Relationship between opening degree and max.
pressure gradient(on-site test)

Annual Report 2021-2022

No. of trials required for optimization

Max. pressure gradient30

Adjust position (conventional)
Adjust opening degree (proposed)

20

10

0

Approx. 80% less

0

10

20

30

40

50

Tunnel entrance hood length (m)Fig. 3 Comparison of no. of trials required for
optimization(model experiment)21

Major Results of Research and Development

19. High-resolution sound source identification method for wayside noise reduction measures• The adoption of a new specially designed two-dimensional spiral array system and the application of
a cutting-edge signal processing method for moving sound sources make it possible to increase thespatial resolution of sound source distribution around vehicles by a factor of about nine.
• Since the new system is capable of visualizing sound source distribution with a spatial resolution of
about the size of a wheel, it can be used to analyze sound sources in bogies, pantographs, and otherparts in detail. It is also useful for verifying the effectiveness of noise reduction measures.
2
1

y (m)

When examining ways to reduce noise along Shinkansen
lines, which is increasing as Shinkansen trains continue toget faster, it is necessary to understand the relevant soundsources distributed around vehicles. For this purpose, wecreated a portable two-dimensional spiral array systemwith a microphone array specially designed for measuringsound source distribution along railway lines (Fig. 1). This
array system has enough spatial resolution to distinguishsound sources near the wheels of each axle when targeting a bogie. We also utilized a method of converting data
recorded by the system, by analyzing it with a generalpurpose signal processing program and then storing it ina database. This enables high-resolution identification ofmoving sound sources using a deconvolution method to
remove sound source “fuzziness” caused by the geometricmicrophone array. This is the first attempt of its kind in
the railway field in Japan. The sound source identification
method is proven to increase the spatial resolution of soundsource distribution by a factor of approx. nine (three times
higher in the railway line direction and three times higher
in the vertical direction) compared to the conventionalsignal processing method, which utilizes the delay andsum method. At this higher resolution, the sound sources

0

-1
-2
-2

-1

0

x (m)

1

2

Specially designed
microphone array(72 points)

Portable frame withstructure that divides into
6 parts

Fig. 1 A two-dimensional spiral array system used formeasuring noise along railway lines(9.1 m from the center of the nearest track)around vehicles can be determined in excellent detail. For
example, sound sources in and around the wheels of eachaxle of a fast-moving Shinkansen bogie and in the gaps
between its vehicles can be clearly discerned (Fig. 2).
The method makes it possible to investigate noise reduction measures for sound sources identified in bogies andpantographs and to demonstrate their effectiveness.

Conventional method

Deconvolution method

Higher resolution

Higher resolution

Fig. 2 Comparison of sound identification between conventional and new deconvolution-based
methods for high-speed vehicles

22

Annual Report 2021-2022

Railway Technical Research Institute

IMPROVEMENT OF CONVENIENCE

20. Method of evaluating the effectiveness of investing indisaster countermeasures for freight transport• We developed a method for quantitatively evaluating the effectiveness of investments in disastercountermeasures, using the volume of freight traffic and the costs to operators during periods whenservices are partially interrupted due to a disaster as indices for evaluation.
• The method can be used for formulating disaster countermeasures and identifying investment priorities,i.e., the areas that most need countermeasures.
As natural disasters become more severe and frequent,effective disaster countermeasures are required to improvethe disaster resilience of rail freight transport. However,quantitatively evaluating the effectiveness of such investments has been difficult. To solve this problem, weconsidered periods when the railway network is partiallyinterrupted due to a disaster. We first developed a methodfor quantitatively determining the volume of freight traffic Q(hereafter, “disaster transported volume”), including detourtransport and substitute truck transport, and the cost torail freight operators L (hereafter, “disaster cost”). When
we verified the validity of the developed method using
actual freight transport data from past disasters, assuming
no change in freight transport demand after the disaster,
we confirmed that this method is capable of accurately
estimating disaster transported volume, with the exceptionof periods when demand drops temporarily due to longconsecutive holiday periods (Fig. 1).
Next, to evaluate the effectiveness of investments in disaster countermeasures, we developed a method focusing on
disaster transported volume and the sum of disaster period

costs L and disaster investment costs I (total disaster cost).
We calculated the difference in the disaster transported
volume, ΔQ (Index 1) and the difference in the total disaster cost Δ(L+I) (Index 2), between the two cases inwhich disaster countermeasures are implemented and not
implemented on railway networks. The disaster transported
volume Q and disaster period cost L are calculated using
the quantification method described earlier. This method
is used to obtain Index 1 and Index 2 for each disastercountermeasure and to evaluate the effectiveness of each
investment (Fig. 2).
This method can be used to formulate various disastercountermeasures, such as improving disaster resilienceby enhancing facilities at freight stations (“hard” measures)and expanding truck substitution routes (“soft” measures),as well as to identify the areas of the railway network
where it is most beneficial to apply measures. By replacing
volume of freight traffic with that of passenger traffic, themethod can be similarly used to evaluate the effectivenessof investments in disaster countermeasures for passenger
transport on the same railway network.

Partial restoration

Disaster dateIndex (2)Δ(L+I)

Precisely reproduces
disaster transported volume

Drop in demand
due to longholidays

Restoration
dateTime (days)

Estimated value

Actual value (7-day moving average)* Actual value: Average of day itself and three preceding and followingdays (total 7 days)(smoothed day-of-week variation in freight demand)

Fig. 1 Results of verification using actual freight transport
data in past disasters

Annual Report 2021-2022

Q1

Q0

I1

L0

L1

Not implemented

Measure 1

Q2
Index (1)
ΔQ

I2

L2
Measure 2

Bar graph: Total disaster cost (L+I)

Periods when services are partially interrupted

Broken line: Disastertransported volume (Q)Volume of freight traffic (Q)Evaluate investment effectiveness based on improvements
in indexes (1) and (2)Measure 1 is a more effective investment
Q: Disaster transported volume

L: Disaster period cost

I: Investment cost of disaster measure

Fig. 2 Evaluation of the effectiveness of investments
in disaster countermeasures based on the difference
between volume of freight traffic and total disaster cost23

Major Results of Research and Development

21. Method of evaluating thermal comfort of solar radiation
environments in commuter trains• We proposed a method for evaluating the comfort of thermal environments in commuter trains, taking
into account the effects of solar radiation.
• We conducted an experiment with participants by simulating a solar radiation environment inside a
train, to confirm that the predictions generated by the proposed method correspond closely to actual
experience.
to calculate the sensible temperature of each body part,
thereby reflecting the physiological state of a passenger.
Next, the average, maximum, and minimum sensible
temperatures of the whole body and a statistical model
based on a past experimental data with participants wereused to predict the proportion of passengers that feel
unsatisfied with a particular thermal environment (“dissatisfaction rate”). Since the model takes into account both
local bodily sensations and the average sensation over thewhole body, it can be applied to complex environments
in which a part of the body is exposed to solar radiation.
The coefficient of correlation between the predicted andmeasured dissatisfaction rates was 0.90, confirming thatthe method is sufficiently reproducible (Fig. 4).
The proposed method can be used to make a variety ofquantitative evaluations from the thermal comfort point ofview, for example to assess the effectiveness of measures
such as improving air conditioning control and reducing
solar transmittance through windows, or to examine theinfluence of opening windows for ventilation.

200

Intermittent variation in solar radiation due
to solar shading of surround structures
Shielding

13:20

13:30

13:40

Sensibletemperature ofdifferent body parts

Input

Head

Solar
radiation

Chest

Temperature
Humidity

Arms●●

Wind speed

●

Feet

[Reflects local sensations in addition
to whole-body average sensation][Local sensations]Max. sensible
temperature
Min. sensible
temperature

OutputBlood flow regulation

Fig. 3 Overview of proposed method0Fig. 2 Outline of an experiment with participants

Prediction of psychological condition: statistical model[Average sensation]
Average sensible
temperature

100

Assuming sunlight is received through a window behind seat

Dissatisfaction
rate of “too cold” ◎Dissatisfaction
rate of “too hot” ◎Skin temperature,sweating

Synthesis(Average and local)

Prediction of physiological condition

3 sec. intervals

200

Time
Example of intermittent variation in solar
radiation

Fig. 1 Example of measured solar radiation in a train

24

Radiation

100
0
13:10

Solar radiation (W/m 2)

Increase in solar
radiation due tochange in travel
direction

300

Artificial solar illuminator and shielding
tool simulate solar radiation variations
inside a train[%]

100 Red: Dissatisfaction of “too hot”
Measured dissatisfaction rate (%)

Solar radiation (W/m 2)

Passengers often express their opinions about the thermal
environment inside commuter trains, claiming they are too
hot, or too cold.
A variety of factors affect thermal comfort in trains, including
air temperature, humidity, air velocity, and solar radiation.
One of these, solar radiation, which varies significantly withthe position and direction of the running train (Fig. 1), hasa big impact on passenger thermal comfort, but up to now
it has not been possible to quantitatively evaluate thermal
comfort in a way that takes this factor into account. To
understand the physiological and psychological effects ofthis factor, we conducted an experiment with participantsby simulating a solar radiation environment inside a train(Fig. 2). Based on our findings, we then proposed a method
for evaluating thermal comfort that takes the effects of solar
radiation into account (Fig. 3).
Using solar radiation, air velocity, and other parametersthat define the thermal environment inside the train as
inputs, the proposed method uses a thermos physiological model with an integrated thermoregulatory function

Blue: Dissatisfaction of “too cold”

80
60
40

radiation〇 100 W/m2
△ 200 W/m2
□ 300 W/m2
200 W/m 2 isintermittent
radiation

Correlation
coefficient
0.90

20
0
0

* No solar

20

40

60

80

100

Predicted dissatisfaction rate (%)

Fig. 4 Prediction based on proposed method andmeasurements

Annual Report 2021-2022

BASIC RESEARCH

Railway Technical Research Institute22. Earthquake early detection method based on deeplearning of seismic motion• We developed an earthquake early detection method based on deep learning of seismic motions.
• The method improves the ability to discriminate between seismic motion and noise from approximately
90% to 99% and enhances the estimation precision of all seismic parameters, including epicentral
distance and magnitude.
• We confirmed that the method is capable of processing estimates in real time.
have a noise identification rate of approximately 90%, but
we have confirmed that with deep learning, this figure canbe increased to approximately 99% (Fig. 1).
To estimate seismic parameters, we used approximately
20,000 observed seismic motions as the training data,
confirming that the proposed method, compared to thecurrent methods, is capable of significantly improving theestimation precision of current methods for all parameters(Fig. 2). We also showed that even with a general-purpose
CPU, it is possible to complete all processing, from noise
identification to seismic parameter estimation, in as little
as 0.7 seconds, proving that the method is practicable forreal-time applications.
In the coming years, the development of deep learningbased seismograph of early warning system for railway
that incorporate noise identification and seismic parameter
estimation will significantly enhance earthquake resilience.

Vertical motion acceleration (gal)Earthquake early detection methods need to distinguishbetween seismic motion and noise from sources such as
train vibration, estimate epicentral distance, magnitude,
and other seismic parameters, and to determine the riskof damage to railways as soon as possible after detecting
vibration. To accomplish all this, it is important to increase(1) the precision of discrimination between seismic motion and noise and (2) the precision of seismic parameter
estimation. In view of this, we developed an earthquake
early detection method featuring integrated deep learningand evaluated its precision.
Previously observed seismic motions and train vibrations
are used as training data for identifying noise. Even when
the number of train vibration records at a seismic observation station is small, it is still possible to achieve a highidentification rate by increasing the volume of training databy means of a data generation process. Current methods

Seismic motion

Train vibration
Distinguish in 1 sec. from start ofvibration
Current method

Deep learning

Identification
rate of seismic
motion

89.8%

99.9%

Identification
rate of train
vibration

90.0%

98.5%

Fig. 1 Discriminating between seismic motion and train
vibration in an earthquake early detection system

Annual Report 2021-2022

100
80
60
40
20
0
-20
-40
-60
-80
-100

Start of P-wave

01

As little as 1 sec. after
start of P-wave, estimates:
・Epicentral distance・Back azimuth
・Magnitude

2

Time (s)Seismic parameters
element

Improvement in estimation precision
when using deep learning, comparedto current method

Epicentral distanceUp 39%

Back azimuth

Up 4%

Magnitude

Up 27%

Fig. 2 Estimating seismic parameters in an earthquake early detection system

25

Major Results of Research and Development

23.HILS for the current collection systems using a HighSpeed Test Facility for Pantograph/OCL Systems• Using a High-Speed Test Facility for Pantograph/OCL Systems, we developed a HILS for the current
collection systems that takes into account the effects of overhead contact line (OCL) vibration, sliding
due to travel, stagger of the OCL, environmental temperature, humidity, and energization.
• The HILS for the current collection systems enables comprehensive evaluation of pantographperformance based on a bench test, making it possible to develop higher-performance pantographs
more efficiently and inexpensively.
To evaluate pantograph performance, it is important to conduct
evaluation in an environment with overhead contact line (OCL)
vibration, as well as sliding due to travel and stagger of the
OCL. We previously developed a system capable of evaluating
pantograph performance under the influence of OCL vibration up to high frequencies, as a HILS (Hardware-In-the-Loop
Simulation) for the current collection systems. However, themethod was unable to take into account the effects of sliding or
stagger of the OCL. To resolve this issue, we embedded a HILS
for the current collection systems in a High-Speed Test Facility
for Pantograph/OCL Systems (HiPaC). Although the OCL vibrations that can be considered with this HILS system are limited
to low frequencies, the system can take into account the effects
of sliding and stagger of the OCL, as well as temperature rises
and component wear due to ambient temperature, humidity, andenergization (Fig. 1). In the HiPaC, the mass of the moving part

Numerical simulation ofoverhead contact lines

HILS: Overhead contact line vibration
can be taken into account up to
support point interval frequency

is large compared to that of the previously developed HILS forthe current collection systems and the frequency characteristics
are also more complex. For this reason, developing a HILS
compensator suitable for the HiPaC makes it possible to take
into account OCL vibrations up to the frequency at which thepantograph travels from one supporting point to another (approx. 2 Hz). As a result, evaluations of the uplift of the contact
wire at the supporting points through which the pantographpasses, which is an important indicator for the safety of the
OCL systems, as well as selection of an appropriate damper
constant for the pantograph, can now be conducted based ona bench test (Fig. 2). Previously, such evaluations could only
be made by on-track testing. This new method therefore makes
it possible to develop higher-performance pantographs more
efficiently and less expensively.

High-Speed Test Facility for Pantograph/OCL Systems (HiPaC)

Previous HILS: Takes into account overhead contact line
vibration up to dropper interval frequency (approx. 20 Hz)

・Overhead contact line vibration
・Sliding
・Stagger of overhead contact line
・Ambient temperature
・Humidity
・Energization

Hydraulic exciter
・Overhead contactline vibration

Low-frequency overhead contact line vibration can be visualized under avariety of conditions

Specialized in overhead contact line vibration up
to high frequencies

Pantograph headdisplacement (mm)Fig. 1 Relationship between HILS using HiPaC and conventional method: Supporting point

40
30

HILS with HiPaC

20

Overhead contact line/pantograph simulation

100

Close match with simulation

450

500

550
Distance (m)600

Uplift on supporting points

Uplift of the contact wire on supporting points can beevaluated based on a bench test

Fig. 2 Comparison of HILS test and simulation at a travelling velocity of 300 km/h
(Simulation is done under ideal conditions, without overhead contact line irregularities)

26

Annual Report 2021-2022

Railway Technical Research Institute

24. Mechanism of increasing wear rate of contact wires
near pantograph stopping positions of Shinkansen• An investigation within a Shinkansen station yard clarified the train-speed dependency of contact
wire wear rate.
• The study revealed that the coefficient of friction increases at low speeds, thereby accelerating“delamination wear,” which is the cause of the high wear rate.
• We confirmed that the application of grease can reduce contact wire wear in low-speed sections ofrailway lines.

Wear increases at low
speeds0.1
0Inside60 km/h position

1.2

0.3
0.2

cross-sectional metallographic structure of actual contact
wires using an electron microscope (Fig. 3), it is likely thatwear rate increases at low speeds because an increase incoefficient of friction promotes a type of wear in the formof peeling known as “delamination wear.”
As a measure to limit delamination wear, we consideredusing lubrication to reduce the coefficient of friction. For
this purpose, we fabricated a new laboratory test facility
capable of simulating low-speed travel conditions (Fig. 4)
and investigated the impact of grease on wear reduction.
Our findings confirmed that the wear rate was reduced by
75% or more as long as the grease adhered to the contact
wire (Fig. 5). We are now planning to verify the sustainability of the wear-reducing effect of external lubricationin operating trains.

1.4

Shinkansen Station A
Shinkansen Station B

0.4

Coefficient of friction

Contact wire wear rate (mm/104pass)

The wear rate of the contact wires of Shinkansen is highwithin station yards and other low-speed sections of
track. For this reason, contact wires need to be replaced
frequently, frustrating efforts to reduce maintenance costs.
To understand why wear rate is high at low speeds, we
examined the relationship between the wear rate of contact wires and travel speed, observed the cross-sectional
structure of worn contact wires, and conducted sliding
experiments over a wide range of speeds.
Data on the wear distribution of contact wires and train
travel speed in a station yard revealed that the contact wire
wear rate has the characteristic of increasing rapidly at low
speeds (Fig. 1). Based on measurements in experiments
to determine the train-speed dependency of the coefficientof friction (Fig. 2) and observations and analysis of the

1

50 μm

0.8

Coefficient of friction
increases at low speed

0.6

Frictionsurface

Stopping position

0.4
0.2

0

20

40

60

80

0

100

Speed (km/h)

High strain

0

100

200

300
Strain: Min.

Speed (km/h)

Fig. 1 Train-speed dependency ofcontact wire wear rate

Max.

= Strong frictional
force works topromote wear in
the form of peelingFig. 3 Example of strain analysis ofactual contact wire cross-section

Fig. 2 Train-speed dependencyof coefficient of friction

ve s

for w

a

ck
Actual pantograph

0.08
0.04
0

Fig. 4 New testing facility

Annual Report 2021-2022

2nd timeMoard / b

1st time

Actual contact wireContact wire wear rate
(mm/104pass)

0.12

No grease application

With grease application

Fig. 5 Wear-reducing effect of contact wire by applying grease27

Major Results of Research and Development

25. Text analysis codes for human error analysis• We created an algorithm (code) for text analysis to analyze work characteristics, psychological factors,
and error behaviors described in risk information from accidents and potential incidents.
• The code can analyze 95% of approximately 4,000 items of the descriptive data of error behaviors ina few seconds, whereas it would take a human several months to classify them.
To utilize risk information about accidents or potential
incidents for safety management purposes, it is necessary
to organize and analyze collected information. However,
although the number of railway accident reports is notlarge, there is a large volume of descriptive textual information due to the detailed investigations and analyses
that are done for each incident. It is therefore a laborious
task to organize all the information. At the same time, thenumber of voluntary reports about potential incident issues
has risen in many organizations over the past decade, inline with growing safety awareness and the cultivation ofa stronger workplace safety climate. This trend has madethe burden of organizing risk information even heavier.
In light of this, we created an analysis algorithm (code)
for applying text mining technology to the descriptive dataof risk information. A notable feature of this algorithm isthe creation of classification codes for identifying trends

Perception missBehavior missQuantity/timing gap
Excessive/different behavior

Error behaviors (4 items)
Psychological factor(18 items)in the occurrence of human errors, including 162 work
characteristics (scene, location, object, conditions, etc.),
18 psychological factors, and 4 error behaviors (Fig. 1).
Risk information analysis based on these classification
codes revealed that the quality of reported error behavior
varies according to the type of risk information—for example, accident or potential incidents (Fig. 2). Additionally,
when work characteristics were summarized by reporting
month, we were able to identify the environmental factorsthat required attention depending on the season (Fig. 3).
This method makes it possible to analyze 95% of 4,000
items of descriptive data in just seconds, compared tothe several months it would take a human to classify thesame error behaviors. We also confirmed that if the resultsof a human classification are assumed to be correct, theaccuracy of the algorithm is in 83 to 94% (rate of matching).

ExampleLack of understanding
Assumption
Distinction

Work characteristics (162 items)
ObjectExampleInside of site
Curve
Wooded area

Protector

ATS
Timetable

ExampleLocation
ExampleExampleScene
Brake operation
Contact
MovingCondition
Weather
Delay
Malfanction

Fig. 1 Outline of text analysis codes

020

Applicable percentage (%)

40

60

80

100

Type of report data

Railway
accident
Work
accident
Railwayincident
Potential
incident

Applicable percentage (%)

20
Weather

15
10

Wooded Area
External・Flying objects
Disturbance ・Animals/ Insects
Wetted Area

5

Perception missBehavior missQuantity/timing gap
Excessive/different behavior

0Timetable

4

5

6

7

8

9

10 11 121

2

3Month of occurrence

Fig. 2 Applicable percentage of error behavior(Usage example 1)

28

Fig. 3 Occurrence frequency of work characteristics items
by reporting month (Usage example 2)

Annual Report 2021-2022

Railway Technical Research Institute

26. Method of visualizing sound wave propagation forclarification and prediction of railway noise• We developed a measurement method for visualizing sound waves propagating through the air basedon laser polarization and used it to visualize sound waves diffracting through sound barriers and the
Doppler effect caused by a moving sound source.
• We developed a method for estimating the sound pressure distribution in a specific cross-section
based on sound wave visualization results.
• The method can be used to shed light on the phenomenon of railway noise and increase the accuracyof noise prediction methods along railway lines.
Railway noise is characterized by the fact that soundsources move at high speed and by the presence of reflective objects such as vehicle bodies and sound barriers
around the sound sources. So, to understand and predict
this phenomenon, it is important to grasp the propagationof sound. We therefore developed a method for visualizing
the propagation of sound emitted from a moving source
through the air and the diffraction behavior of sound waves
near a sound barrier.
To visualize sound waves, we used a parallel phase-shifting
interferometer (Fig. 1). This device captures images of theinterference fringes of laser light with a high-speed camera,
making use of the fact that pressure fluctuations in the aircaused by sound can be observed as phase changes in
the light. We also developed a method for estimating thesound pressure distribution within a specific cross-section
based on the theoretical equations for spherical waves.
Estimate sound pressure
Parallel phase-shiftingin any cross-section
interferometer

By applying this method to a 1:80-scale model of a noisebarrier, we confirmed that it is possible to visualize thereflection, interference, and diffraction attenuation of soundwaves observed around the noise barrier (Fig. 2). Usinga model launcher system to visualize the wavefronts ofsound emitted from a moving sound source—a modeltrain fitted with an ultrasonic transducer—we confirmed theability to observe frequency modulation due to the Doppler
effect (Fig. 3). This is the first time that the Doppler effecthas been experimentally visualized in the air. With thismethod, we expect to be able to validate a physical modelof multiple reflections between vehicle bodies and soundbarriers and to evaluate the reflection, interference, and diffraction of sound waves under realistic railway structures.
All this will be useful in further improving the accuracy ofnoise prediction methods along railway lines.

Measurement region
Laser

Interferometer
High-speed
cameraSound source
Laser

Fig. 1 Outline of sound wave visualization experiment0
Diffraction

Sound pressure amplitude

Reflection and interference＋

Image of soundpropagation in an actual
train

ー

Sound source(stationary)Sound barrier

Fig. 2 Visualization of sound wave reflection, interference and diffraction with a sound barrier(model experiment)

Annual Report 2021-2022

Low frequency
High frequency
Sound source(long wavelength) moves at 100 km/h (short wavelength)

Fig. 3 Visualization of the Doppler effect and wavefronts of sound propagated from a moving source(model experiment)

29

News
Release
1. Mr. Nakamura of RTRI Commended for his Contribution toStandardization Activities
Mr. Kazuki Nakamura of RTRI was given the Year 2020 Award for Contribution to Standardization by
the Railway Technology Standardization Survey Committee. The award giving ceremony was held on
March 31 in Tokyo.”Award winner:
Mr. Kazuki Nakamura, Laboratory Head,
Telecommunications and Networking, Signalling andTransport Information Technology Division
Award winning achievement:
Since 2017, Mr. Nakamura has contributed to the review activities in Japan as a member of the task force andpanels for railway telecommunications of the International
Telecommunication Union (ITU-R) and the Ministry ofInternal Affairs and Communications. In addition, heparticipated in the International Special Committee onRadio Interference of the International ElectrotechnicalCommission (IEC/CISPR/SC B/WG 2) as an international
expert for more than 10 years and contributed to domestic
and international review activities.
He joined the Japanese delegation for the 2019 WorldRadiocommunication Conference (WRC-19) under ITU-R.

He was given the award because he presented Japanese
views at the conference with other members, succeededin reflecting them in the resolutions and is expected tofurther contribute to the international standardization
efforts.
* Award for Contribution to Standardization is given
to persons who contribute to international and domestic
standardization activities in the railway field. This award
giving is started in 2007 by the Railway Technology
Standardization Survey Committee (Chairperson: Hiroyuki
Osaki, Prof. of the University of Tokyo Secretariat: Ministryof Land, Infrastructure, Transport and Tourism and RTRI)
for the purpose of supporting the activities of award
winners and raising the awareness of the importanceof standardization among related people. In particular,
the Award for Contribution to Standardization is given
to a person who is expected to continue to contribute tostandardization.

Mr. Kazuki Nakamura

30

Annual Report 2021-2022

Railway Technical Research Institute

2. High-Speed Wheelset Dynamic Load Test Facility
Completed
On February 26, 2021, a new test facility was completed at RTRI. This test stand is capable of reproducing high-speed train running (maximum 500 km/h) and the load acting to the bogie (Fig.1 and 2),
and, with this facility, durability and performance of Shinkansen’s wheel and axle can be evaluated. It
will be utilized to evaluate the durability of wheelset and bogie parts and to analyze the mechanism ofwheelset damage development.
[Outline of the test facility]•Instead of running a test bogie on a track, this facility
is capable of reproducing 500 km/h running by rotating
the wheelsets of a test bogie on its high-speed roller.
•Capable of reproducing vertical vibration of a track byshaking the roller vertically.
•The loading frame that simulates a carbody is shaken
by the vibration exciters and reproduces vertical andlateral vibration of a running train carbody and the load
acting to the bogie. (Fig. 2)•Simulates the torque acting to the wheelset during
acceleration and deceleration.*•Capable of testing bogies for different gauges as it
has rollers of Shinkansen gauge and conventional gauge.
*Simulating the torque to move a carbody in the longitudinal direction with a flywheel.

Fig. 1 High-speed wheelset dynamic load test facility

Annual Report 2021-2022

31

News Release

Fig. 2 Configuration of high-speed wheelset dynamic load test facility

Table 1 Specification of high-speed wheelset dynamic load test facility

32

Annual Report 2021-2022

Railway Technical Research Institute[Tests and evaluations using the test facility](1) Durability assessment
Using this test facility, durability of the components
of a wheelset and a bogie can be evaluated under theconditions close to running tests with an actual vehicle.
The results of the evaluation will be utilized in the research
to assess the service life of these components and inextending inspection intervals for the purpose of achieving
labor-efficient maintenance.
(2) Performance assessment
Vibration property and thermal property of the components of a wheelset and a bogie will be evaluated under
the loading conditions simulating real train running or
harder conditions. The results will be used to improvethe bogie performance.
(3) Damage cause analysis
Through the tests on this facility under the conditions
simulating the vehicle load and acceleration/deceleration
torque, causes of damage to the components will be analyzed and effects of preventive measures will be verified.

Annual Report 2021-2022

33

News Release

3. Low-Noise Moving Model Test Facility Completed at RTRI
A low-noise moving model test facility was newly completed at RTRI. This testing facility, featuring theworld’s highest class performance, is capable of running a 1:20 scale model train at the maximum speedof 400 km/h and accurately reproducing the air flow and turbulence created by a running train. It wascompleted on July 10, 2020 and testing was started on October 29, 2020.
[Overview of low-noise moving model test facility]
This testing facility consists of three sections: launching section, measuring section, braking section (Fig.1)
In the launching section, a 1:20 scale model train islaunched and accelerated to maximum 400 km/h by aroller rig. (upper right of Fig. 1) The model train has a
streamline nose shape replicating an actual train’s front
shape (Fig. 2).
In the measuring section, the model train coasts inorder to reduce background noise. This section is a semianechoic chamber and its walls and ceilings were covered

with sound-absorbing material to reduce reflected sound(Fig.2). This semi-anechoic chamber enables measuringof pressure fluctuation in an open section of a tunnel(low-frequency-range aerodynamic noise generated bya train). In addition, by mounting a tunnel model in thischamber, micro-pressure waves propagating from tunnel
ends and pressure and wind speed fluctuation in a tunnel
can be reproduced.
In the braking section (upper left of Fig.1), the modeltrain runs into foamed polystyrene beads placed in the
32 meter-long section and comes to a stop.

Fig. 1 Low-noise moving model test facility and basic specification

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Railway Technical Research InstituteFig. 2 Measuring section
Safety guard is tentatively mounted for speed-increase testing[Planned testing and experiments]
High-speed trains generate turbulent air flow whichcauses aerodymnamic noise and the pressure fluctuations around the trains have impacts on insulating panels
and tunnel walls. The low-noise moving model test facility
will be used to analyze these phenomena and to develop
measures to solve the problems.
(1) Experiments to analyze the phenomena causedby passing trains.
The research into the mechanism to generate lowfrequency-range aerodynamic noise will be implemented
and its results will be used to develop mitigating measuresand to confirm their effects. By evaluating the impacts on
structures including noise-insulating walls, tunnel walls
and tunnel hoods, the data will be used for the strength
design of structures.
(2) Experiments to reduce micro-pressure waves
When a train runs into a tunnel, micro-pressure waves
radiate from the other end of the tunnel. Experiments
will be conducted to improve the nose shape of a highspeed train in order to mitigate the micro-pressure wave
radiation and to assess the performance of the mitigating
hoods mounted to tunnel ends.

Annual Report 2021-2022(3) Gathering data to develop numerical simulation
methods
Using the test data accurately reproducing actual phenomena, we will develop numerical simulation methods topredict precisely the phenomena caused by train passing
and radiation of tunnel micro-pressure waves. The simulation methods will be used for strength designs to ensure
necessary strength of structures for higher-speed trains
and new vehicles and to develop measures to mitigate
micro-pressure waves.
[Reference]
RTRI has also had an older testing equipment for thesimilar purpose with the following performance:
Scale of a model train: 1/80 to 1/100
Maximum launching speed: 550 km/h
Shape of a model train: the model has circular crosssection shape, but the ratio of cross section area to vehicle
length is equivalent to that of an actual car ( Fig. 3 )35

News Release

Fig. 3 Model train of the older equipment

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4. RTRI and the National Science and Technology
Development Agency of Thailand Conclude Memorandumof Understanding on Technical Cooperation
On July 1, RTRI and the National Science and Technology Development Agency of Thailand (NSTDA)
concluded Memorandum of Understanding on Technical Cooperation.

NSTDA is an independent institute under the Ministry of
Science and Technology of Thailand which is responsible
for research and development of overall technologies.
Since 2011, RTRI and NSTDA have been sharing technical information and expertise through sending researchers
for railway technical seminars in Bangkok and accepting
visitors to RTRI from NSTDA. Since 2016, RTRI has
been cooperating with NSTDA in organizing a railway
technical tradeshow in Thailand. Furthermore, the twoorganizations have already started to build a cooperative
relationship in the field of railway technical standards.
Since fostering a closer technical cooperation with
NSTDA contributes to overseas development of Japanese
railway technologies and further development of railway
systems in both countries, RTRI concluded the memorandum with NSTDA.
Considering the current Covid-19 pandemic, Dr.
Ikuo Watanabe, President of RTRI, and Dr. Narong
Sirilertworakul, President of NSTDA, signed and exchanged the memorandum by postal mail and shared
video messages.
This memorandum is the 10th agreement that RTRI
has concluded with overseas institutions regarding technical cooperation.
Major activities under this memorandum are as follows.
Further details will be discussed and determined later.
(1) Knowledge, information, skills and experience will
be shared, developed and improved.
(2) In the process of preparing joint activities, both parties send researchers to each other and have meetings.
(3) Collaborative research, meetings, lectures, symposiums and research projects will be organized on the
topics in which both sides are interested and/or which are
beneficial for both sides.

Annual Report 2021-2022(4) Information will be shared regarding the progress
and future plans of activities in specific fields.
(5) Activities to strengthen the cooperative relationships will be encouraged as much as possible.
[Message by Dr. Ikuo Watanabe, President of RTRI]
I am very much pleased to be able to conclude theMemorandum of Understanding on Technical Cooperation
with NSTDA. Since 2011, RTRI and NSTDA have been
cooperating and sharing technical information and expertise through organizing “professional training course
for railway engineering trainers” in Bangkok, participatingin the Thailand Railway Industry Symposium and Expo
(RISE) hosted by NSTDA, and accepting visitors to
RTRI from NSTDA. Since Thailand is the most important
country in the Southeast Asia and NSTDA leads railway
research and development in Thailand, it is very muchsignificant for both RTRI and NSTDA to conclude thismemorandum of understanding on technical cooperation.
In addition to the cooperation and exchanges we havecontinued so far, we would like to further cooperate with
NSTDA in international railway standards developmentthat ISO and IEC are addressing in order to include Asian
railway technologies in the international standards.
I am very sorry that I am not able to talk directly with
Dr. Narong Sirilertworakul at an in-person ceremony. Now
vaccination has been accelerating both in Thailand andJapan, and I am looking forward to further promoting
our cooperation and exchanges after Covid-19. I hopethat Thailand railways will further develop and Japan and
Thailand, RTRI and NSTDA will be having even closer
relationships.
[Message by Dr. Narong Sirilertworakul, President of
NSTDA]On behalf of National Science and Technology
Development Agency (NSTDA), Ministry of Higher
Education, Science, Research and Innovation (MHESI),

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Thailand, I would like to thank all of you, viewing thisvideo message. Although we could not be able to celebrate our MOU signing ceremony in-person under thisdifficult circumstances, we would like to do our best, in
this manner, to carry on the establishment of our formal
relationship and collaborative activities with our partner,
Railway Technical Research Institute.
Our close relationship has started in 2011. NSTDA
received great support from RTRI when we first organized“professional training course for railway engineering trainers” in Thailand. Since 2016, NSTDA and RTRI havecontinuously co-hosted the Thai Rail Industry Symposiumand Exhibition (RISE) in Thailand. The RISE event is
an academia-research-industry platform for sharing
knowledge, know-how and experiences among Thaiand international experts, researchers, engineers, andtechnicians. This kind of event is so important for Thailand
to realize our goal to be a logistics hub in ASEAN.

Today, RTRI and NSTDA have come to conclude a
3-year MOU on Technical Cooperation in the field of railway technology. We are very much hoping that, throughthe exchange of information and know-how and thisresearch collaboration, the two institutes will join hands
to work and contribute to the advancement of railway
systems and further strengthen the bilateral partnership
between Thailand and Japan.
.

Fig. 3 Model train of the older equipment

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5. RTRI’s Rolling Stock Test Plant constructed in 1959
designated as Mechanical Engineering Heritage
On August 7 this year, the Rolling Stock Test Plant (Fig.1) at RTRI was designated by the Japan Societyof Mechanical Engineers(JSME) as Mechanical Engineering Heritage No. 108 because of its historicalvalue as an existing mechanical and engineering asset.

[Overview of the facility]
・The facility was constructed in 1959 by the predecessor of the current Railway Technical Research Institute(RTRI), which was a research wing of the former Japanese
National Railways. Its purpose was to modernize railway
traction systems and increase running speeds. Testing
was started in 1960.
・High-speed running can be reproduced as a benchtest with an actual vehicle. The rail-shaped rollers rotate athigh speeds and are capable of simulating train running.
The rollers can also add vertical vibration.
(Reference)
Gauge of roller: adjustable between 1000 mm to 1676mm
Speed: Up to 250 km/h with a vehicle, 350 km/h witha bogie・It can be used for tests to confirm vehicle performance, including stability at high speeds.
・Test results were used to determine specifications
for the test vehicle bogie prior to the opening of Tokaido
Shinkansen. The Series 951 test vehicle for Sanyo
Shinkansen was also tested at this facility (Fig.2) and thisfacility made a significant contribution to the developmentof Shinkansen.
・In 1990, RTRI constructed another high-speed vehicle test facility capable of testing at a maximum speedof 500 km/h in order to increase competitiveness ofrailways over other transport modes. Since then, vehicle
running tests have been conducted at the new facility.
The historical facility still serves for tests of inverters andother components.

Fig. 1 Rolling Stock Test Plant

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Fig.2 Sanyo Shinkansen test vehicle tested on this facility[Note]In 2007, JSME started to designate objects which areexisting in Japan and have historical significance in thedevelopment of machine technology and engineering as
Mechanical Engineering Heritage. This designation helps
to preserve historical items and pass them on to the next
generation as cultural heritage. JSME has designated 104
items prior to 2020.
JSME website
https://www.jsme.or.jp/kikaiisan/#section1

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6. RTRI Develops Facility Gauge Measuring Equipment
RTRI developed a device to detect trackside facilities protruding into the track clearance (Fig. 1). This
device is capable of checking continuously whether any trackside objects interrupt safe train runningor not without manual measurement nor using a specialized measuring vehicle.
[Features of the device]・By mounting this device to an existing track inspection car, the required track clearance can be checked in
the regular track inspection without conducting additional
inspections using a specialized vehicle.
・Using a laser sensor, this device enables continuous
measurement of the clearance between train vehicles
and trackside facilities and checking whether required
clearance is maintained on the track both in the daylightand night time. The laser sensor measures the distance
between vehicles and trackside facilities including signalling equipment and level crossings facilities by laser beamreflection time. Since the device can conduct measurement in 80 km/h running, quick detection is possible.
・Its data management unit (Fig. 2) automatically crosschecks the measured data with the facilities management
registry and outputs the results confirming whether track
clearance is blocked or not. It is capable of confirming the
distance from selected trackside facilities as well.
・With this device, it is possible to confirm the sufficient
clearance of 75% of the trackside facilities which havebeen checked manually by maintenance staff so far.

[Background]
In order to ensure safe train operation, building limits
have been set for railway trackside facilities to keep sufficient clearance around a train. Railway operators build
and maintain structures and facilities so that they do notblock the required clearance.
The distance from a specific structure or facility has
been measured manually by maintenance staff or by aspecialized measuring vehicle so far. However, manual
measurement requires a lot of time and labor, and forvehicle measurement, a specialized vehicle needs to be
introduced.

Fig. 1 Facility Gauge Measuring Equipment

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News Release(a) List of facilities(b) Measured result of a facility

Fig.2 Screen display of the data management unit

[Introduction to commercial operation]
Since trackside facilities are screened with this device
in advance in order to identify those which do not needfurther measurement, 75% of the facilities do not requireon-site manual measurement by maintenance staff.
In April, 2021, Kyushu Railway Company introduced
this device to manage about 180 thousand items ofground facilities of conventional lines including signals,
signs, tool boxes and power poles.
As the measurement data is 3-dimensional (Fig. 3)
and cross-sectional shapes of tracks can be measured,
RTRI is considering the possibility of using this system
to measure distance between track centers.

(a) Image by ordinary camera(b) Visualized image of 3-dimensioal data measured by
the device(Red line shows building limit)

Fig. 3 Example of data measured with this device

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7. RTRI Develops Snow Accretion Simulator
RTRI developed the first snow accretion simulator in Japan. This simulator is capable of precisely
reproducing the process of snow accretion onto train bogies by computing and the phenomenon canbe analyzed under conditions close to actual train running including snowfall and wind intensity. This
new device will be used to develop vehicles preventing snow accretion more effectively.

1. Background
While a train is running in snowy areas in winter,the snow on the track is blown off, is accreted onto thevehicle underfloor and bogies, and eventually grows intolarge chunks (Fig.1). Since it takes a lot of cost andmanual work to remove the accreted snow, it has beenrequired to develop vehicle shapes less likely to inducesnow accretion. However, as the research on vehicle
shapes has been implemented using actual vehicles sofar, too much efforts have been required and the testinghas been possible only in winter and snowy times. In
the meantime, impacts by train running such as wheel
rotation and winds cannot be accurately reflected on thetesting in the snowfall wind tunnel using a model train.

The snow accretion simulator has been developed inorder to solve these issues and to reproduce the accretion
process more precisely.

Fig. 1 Snow accretion found on an actual vehicle

2. Overview of the simulator
Using a super computer, this simulator has made possible high-speed calculation under different conditions(Fig. 2).

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News Release

Fig. 2 Simulated snow accretion on a bogie[Features of the simulator]
・Capable of calculating snow accretion under the
impacts of train running such as wheel rotation and winds
against a vehicle.
・Capable of simulating snow accretion onto objects
with complicated shapes like train bogies.
・Capable of visualizing the accretion process including air flow, flying snow particles and snow accreted stateand can be used to analyze the causes of snow accretionand to develop preventive measures.

・The simulator repeats three categories of calculations: calculations to analyze airflow around the object, totrace movements of flying snow particles and to assess
the growth of snow accretion. With these calculations, it iscapable of reflecting on the simulation the airflow aroundthe object which changes depending upon surface shapesof the growing snow accretion and of reproducing theprocess more accurately (Fig.3) .

Fig. 3 Calculation system of the simulator

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Railway Technical Research Institute(a) Result of simulation(b) Result of wind tunnel testingFig. 4 Snow accretion on a cubic object

・By comparing the results of this simulation and of thetests in the snowfall wind tunnel using a cubic test object,
it has been confirmed that this simulator reproduces snow
accretion with 6% error margin in terms of the area ratio
from a lateral view (Fig. 4). In addition, the calculation
results of the maximum thickness and weight of accreted
snow on the complicated-shape bogie are almost consistent with those of wind tunnel tests (Fig. 5).

(The simulation is conducted without rotating thewheels in order to compare the results under the same
conditions)Fig. 5 Snow accretion on a train bogie

3. Research plan using the simulator
RTRI plans to use this simulator to improve vehicle
shapes in order to reduce the snow accretion around bogies. Since the simulated snow accretion process can beincorporated in developing shapes of bogie areas, theefficiency of vehicle development can be raised.

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News Release

8. RTRI Develops a Dynamic Gauge and Twist Measurement
Device TRACK²er
RTRI Developed a dynamic gauge and twist measurement device TRACK²er. This device is capableof measuring track displacement caused by passing trains. Since this device can be mounted on amaintenance vehicle instead of using a track inspection car, it is possible to measure track displacement
more easily, with lower cost.
[Background]
If track geometry is distorted, running safety and ride
comfort of trains will be affected. Railway operators measure track irregularity (consisting of 5 items: longitudinallevel irregularity; alignment; gauge; cross level; twist)
and use the data in repair work and in maintaining adequate track conditions. Track irregularity includes “static
irregularity” which occurs without train load and “dynamic
irregularity” caused by the load of passing trains. Railway
operators monitor and manage either of the irregularities.
It is more effective, in terms of derailment prevention, tomanage dynamic gauge and twist.
Dynamic track irregularity is mainly measured by aspecialized track inspection car equipped with measuring
devices. However, this method requires a specialized
vehicle and a lot of careful preparation work to measure
a number of tracks including service tracks.

・Rail profiles are shown as figures. (Fig. 3 left) The
figure indicates measurement points on the rail used to
calculate track displacement. In case an outlier is detected, it is possible to distinguish whether it is due to ameasurement error or to track irregularities.
・The cost of TRACK²er is about one tenth of theprevious on-board device developed by RTRI and thisdevice can be mounted on or dismounted from a vehicle
in an hour.
・By adding an optional remote controlling function,measuring with this device can be started and stopped
from personal computers at the office.

[Features of TRACK²er]
・Since TRACK²er is a small-sized device(940×220×270 mm) and can be mounted on maintenancevehicles and commercial trains (Fig. 1), it is capable ofmeasuring dynamic gauge and twist during maintenancework and commercial operation and makes the planningof measurement easier.
・Rail positions are measured by a 2-dimensional laser
sensor. The tilt of the sensor toward the track is adjusted
by an angle sensor (Fig. 2). The measurement error
margin is 0.5 mm for gauge and 1.0 mm for twist, whichmeans the same level accuracy as that of the measurement by a track inspection car.
・The measurement data is shown in tabular format.
(Fig. 3 right) The points exceeding the preset standard
value for replacement, repair and other maintenancenecessities are shown in red color and oversights canbe prevented.

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Railway Technical Research InstituteFig. 1 TRACK²er mounted on a maintenance vehicle

Fig. 2 Sensor unit

Fig. 3 Display of results[Improving running safety]
TRACK²er enables dynamic measurement of gauge
and twist without using conventional track inspection carsand contributes to improving running safety of trains.
TRACK²er is a registered trademark of the Railway
Technical Research Institute (registered trademark
number 6442444).

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News Release[Reference]
Track irregularity:
Displacement and deformation of each part of railway
tracks caused by repeated train passing and natural
phenomena. Track irregularity includes following 5 factors.

*1 Slack: Gauge allowance designed to enable trains to pass curve sections smoothly*2 Cant : Height difference between outer and inner rail in curve sections designed to enable trains to passcurve sections smoothly

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9. Seismic Reinforcement Method for Deteriorated Steel
Bridges Developed and Used on a Commercial Line
RTRI Developed an aseismic reinforcement method for steel bridges integrating steel girder, abutment
and embankment (Fig. 1) and it has been applied to a commercial railway bridge. This method isparticularly effective in reinforcing deteriorated steel bridges in urban areas where only little work space
is available. Since this method does not require construction of a temporary track and only minimal
under-bridge space is occupied during the reinforcement work, high seismic performance can be attainedwith a shorter construction period and smaller cost.

Fig. 1 Bridge reinforcement method integrating steel girder, abutment and embankment[Background]Some of the existing steel bridges constructed before
1970’s need to be reinforced to improve their seismic
stability, as seismic designs were not well introduced bybefore those past decades. When replacing a deteriorated
bridge with a new one, the space for a crane track anda temporary track is necessary. (Fig. 2 (a)). Reinforcing
abutment also requires a heavy construction machine toinstall ground anchors. (Fig. 2 (b)). Either of them takes a
lot of work time and costs. In urban areas, in particular, it isdifficult to transport and use large and heavy construction
machines or to ensure adequate space for a temporary
track. Furthermore, deteriorated steel bridges require a
lot of maintenance work including corrosion measures forgirders and supports.

by integrating abutment and embankment with nail-reinforced soil, by connecting the steel girder and abutment
with reinforced concrete.
・The entire stability of a bridge is enhanced by integrating abutment and embankment with nail-reinforced soil,
those works can be handled with a small-sized machine.
(Fig.2 (c)). In addition, the integrated bridge does not havethe bearing connecting steel girder and abutment, themaintenance work for such section including corrosion
measures becomes unnecessary.
・As this method does not require any work with a large
and heavy machine from the front side of abutment (fromthe under-bridge section), the time and space occupied
during the work can be largely decreased and the work
time and cost will be reduced up to 50 %.

[Outline of the method]
・The entire performance of a steel bridge is improved

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News Release

Fig. 2 Reinforcement work

[The bridge reinforced with this method]
This method was used for the first time to reinforce
a commercial railway bridge on the line of the Odakyu
Electric Railway Co., Ltd. As this steel bridge, constructed
in 1927, is surrounded by roads and houses, this method
was applied. (Fig. 3).

Part of this research was implemented with the railway
technical development grants by the Ministry of Land,
Infrastructure, Transport and Tourism.

Fig. 3 A bridge on Odakyu Line reinforced with this method

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10. New Embankment Repair Method Developed
RTRI Developed an early repair method and aseismic reinforcement method for the embankment
damaged by rainfalls and earthquakes. By applying these methods, the performance of embankments
will be improved, while work period and repair costs will be reduced.

Fig. 1 Early repair and aseismic reinforcement method for damaged embankment[Outline of the methods]
Previously, the embankment damaged by rainfalls andearthquakes (Supplementary fig. (a)) was temporarily
sandbagged and train operation was resumed at slow
speeds (supplementary figure (b)), then temporary retaining walls were installed (Supplementary figure (c)),
sandbags were later removed and the embankment
was reconstructed. (Supplementary figure (d)). This
complicated process required long construction periods
and high costs. (Table1). Since the repair work with theearly repair method uses gabions instead of sandbags,
it does not require such complicated work. It will reducethe construction period and cost, and at the same time,
durability against rainfalls is improved.
In the aseismic reinforcement method, in addition to
the work of the early repair method, the embankment isreinforced with soil reinforcing nails made of steel and soil
cement. This method improves earthquake resistance aswell as durability against rainfalls.

Annual Report 2021-2022[Effects and characteristics]
・In the early repair method, gabions are installed tothe damaged section. Since the gabions are built with wire
mesh sheets on site, the repair work can be done quickly
and flexibly and the embankment can be repaired earlier.
・Since the gabions improve stability of embankments,
it is confirmed by analysis that durability against rainfalls
improves by about 70% compared to the existing repair
method.
・With this method, train operation cannot be resumed
at the stage of the temporary repair unlike the repair workwith the existing method. However, the entire work period
and cost are reduced by 30% and 60% respectively.
(Table 1)
・In the aseismic reinforcement method, reinforcing
nails are added to the early repair method. It is confirmed
in model experiments that the embankment repairedwith this method is capable of enduring 50%-stronger
earthquakes compared to the one with the early repair
method and the cost can be 30 % lower than the existing
method. (Table 1)

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Table 1 Comparison with the existing method and newly-developed methods(5-meter high and 10-meter long embankment)

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Railway Technical Research Institute[Embankment reinforced with aseismic reinforcement
method]This method was used to repair the embankment ofSanriku Railways Rias Line damaged by the typhoon
which hit the eastern part of Japan in 2019. (Fig. 2)

Fig.2: The embankment repaired with the aseismic reinforcement method

Supplementary fig. The existing embankment repair method

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11. RTRI’s Researcher Given an Award for His Contribution to
Railway Technology Standardization
Mr. Kazumasa KUMAZAWA, Assistant Senior Researcher, at RTRI, received the Year 2021 Award forContribution to Standardization by the Railway Technology Standardization Survey Committee. The
award ceremony took place on March 30 at Plaza F in Tokyo.

Award winner:
Mr. Kazumasa KUMAZAWA, Assistant SeniorResearcher, Signalling Systems, Signalling and Operation
Systems Technology Division
Award-winning contribution
Joining a domestic working group to deliberate international standards on railway timetabling since 2016, Mr.
Kumazawa has contributed to working out the Japanesepolicy for international standardization. In particular, he
has been a core person in preparing technical details of
the international standard draft, ISO WD 24675 (Running
time calculation for timetabling - Requirements). He has
also served as an international expert of ISO/TC 269/SC
3/WG 3 (Railway timetabling), appointed by the JapaneseIndustrial Standards Committee, and is expected to further

contribute to these activities. For all these contributions
and accomplishments, the award was given to him.
* Award for Contribution to Standardization is given
to persons who contribute to international and domestic
standardization activities in the railway field. This award
giving is started in 2007 by the Railway Technology
Standardization Survey Committee (Chairperson:
Hiroyuki Osaki, Prof. of the University of Tokyo Secretariat:
Ministry of Land, Infrastructure, Transport and Tourism
and RTRI) for the purpose of supporting the activities
of award winners and raising the awareness of the importance of standardization among related people. The
Encouragement Award has been given to those who are
expected to keep contributing to these activities.

Mr. Kumazawa at the award ceremony

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YEAR 2021-2022: April 1, 2021 - March 31, 2022
Copyright© 2022 Railway Technical Research Institute.
All rights reserved.
Published by Atsushi FURUKAWA, Executive Vice PresidentInternational Division, Railway Technical Research Institute
Hikari-cho 2-8-38, Kokubunji-shi, Tokyo 185-8540 JAPANFind us :WEB: https: //www.rtri.or.jp/eng/index.html

For download :Brochure & Annual Report: https://www.rtri.or.jp/eng/rtri/brochure.html
Other Publications: https://www.rtri.or.jp/eng/publish/