ANNUALREPORT

2020-2021
For the year ended March 31, 2021

Railway
Technical
Research
Institute
Railway
Technical
Research
Institute

Foreword
Ikuo WATANABE
President of the Railway Technical Research Institute

I am pleased to announce that RTRI Annual
Report 2020 is now available. This report outlines and introduces the organization of RTRI,
our master plan, research and development andother activities and the data of many research
fields as annexes. It would be our great pleasure if
you could glance over our progress in fiscal 2020
and it helps deepen your understandings of RTRI.

2021, the researchers at RTRI conducted damage surveys and supported the restoration of thedamaged areas. We have adapted our management system to the new-normal life by introducing
the work-from-home practice and new ways ofliving and, as the result, our research activities
have been continued without disruption under the
renewed work environment.

In fiscal 2020, we made a new step forward. In
April, Master Plan, Research and Development
for Creating the Future of Railways - RESEARCH
2025 was started. This master plan covering thefive years until fiscal 2024 is a roadmap to fulfill
our vision “RISING - We will develop innovativetechnologies to enhance the rail mode so that railways can contribute to the creation of a happiersociety”. In addition, Prof. Masao MUKAIDONO
assumed office as our new chairman in June, at atime I became president, and a new management
system was launched.

This unprecedented crisis caused by the pandemic is still ongoing and our society keeps
changing while fighting back against the spread of
Covid-19 and keeping an eye on the post-Cocid-19
world. At the same time, now a paradigm shift isbeing accelerated in the global world toward thesustainable society featuring the keyword “decarbonization”. Without doubt, railway operatorsand industries will be required to further change
to attain the same goal. Developing the basetechnologies that drive the change is the mostimportant mission of RTRI. We will keep changing ourselves drastically and flexibly and keep
creating high-quality research results. In fulfilling
this mission, continued support and advice fromall of you will be most appreciated.

Meanwhile, in fiscal 2020 our society was hard
hit and shaken by the Covid-19 pandemic. Under
the impact of this pandemic, all the countries
including Japan had to strictly restrain social
and business activities in almost all aspects ofpeople’s life and all the industrial sectors. RTRI
has been no exception. Now we need to implement our business and research activities in amore careful and discreet manner.
Under these difficult circumstances, however, wewere able to steadily implement 271 research anddevelopment projects, 92 of which were completed
by the end of fiscal 2020. Furthermore, in thedisasters caused by the heavy rainfall in July 2020
and another earthquake in Fukushima in February
Annual Report 2020-2021

1

Overview

Contents

Organization

2 Overview
Results of
4 MajorResearch and Development

34 News Release

Board of Trustees

Board of Directors

Auditors

Advisor2

Annual Report 2020-2021

Railway Technical Research InstituteExecutive
Vice
Presidents

ChairmanPlanning Division

Compliance Division

Vehicle Structure
Technology Division
Vehicle Control
Technology Division
Structures
Technology Division

President
Executive
Directors

Administration Division
Miyazaki Test
Center
Accounting Division
Information
Management Division

Power Supply
Technology Division
Track
Technology Division
Disaster Prevention
Technology Division

Health Insurance
Systems Center

Signalling &Transport Information
Technology DivisionInternational Division

Materials
Technology Division

Research &Development
Promotion Division

Railway Dynamics
Division

Wind Tunnel
Technical Center
Director,
JR Affairs
Marketing & Business
Development Division
Director, Sales
Railway Technology
Promotion Center
Railway International
Standards Center

Environmental
Engineering Division
Human Science
Division
Maglev Systems
Technology Division
Yamanashi
Maglev
Test Center
Center for Railway
Earthquake Engineering
Research(As of July, 2021)

Annual Report 2020-2021

3

Major Results of
Research and Development
IMPROVEMENT OF SAFETY
1. Seismic reinforcement method for masonry embankment
type train platforms• A method for seismic reinforcement of masonry embankment type train platforms using polyurea resinand soil nails has been developed.
• A reinforcement design method based on the yield seismic horizontal coefficient and a nomogram forcalculating residual displacement has been proposed.
• The seismic reinforcement of platforms can be conducted at about 70% the cost of the conventional
method for replacing platforms.
In past earthquakes, many of the masonry walls thatmake up masonry embankment type train platforms havetumbled or tilted, and seismic reinforcement methods toprevent such damage are required. Conventional seismic
reinforcement requires the replacement of platforms, whichis expensive. Therefore, we have developed a low-costseismic reinforcement method that does not require replacement of platforms.
The developed reinforcement method combines sprayingof polyurea resin to prevent slip-out of stacked stones, soil
nails to prevent collapse, and angle materials to connect
them (Fig. 1). Since this method does not require the replacement of platforms, it can be conducted for about 70%
the cost of conventional methods. We also have proposeda design method for seismic reinforcement of the masonryembankment type train platform that combines the “yieldseismic horizontal coefficient” obtained by calculating thestability at each level of the stacked stones during anearthquake and the “nomogram for residual displacement
calculation” obtained by large deformation analysis using
the meshless method to calculate the residual displacement of the masonry embankment type train platform (Fig.
2).
Using a full-scale model of a masonry embankment typetrain platform reinforced with the proposed design method,
we verified the effect of seismic reinforcement under alarge earthquake (maximum acceleration of 1000 gal), andfound that the displacement at the top of the masonry wall
was less than the limit value, confirming the effectivenessand validity of this design method for reinforcement (Fig. 3).

Polyurea resin
Angle material

Stability calculation
methodCalculate the minimum yieldseiscmic horizontal coefficient

Damage situation

Inertia force

Residual displacement (mm)Displacement (mm)Seismic reinforced structure
Conventional: Replacement

Proposed: Reinforcement

TensionSelf-weight
Support point

Soil nailEarthpressure

Frictionalforce

Calculate the yield seismic horizontal
coefficient at the support point of each level
Large-deformation analysis (meshless method)

Yield seismic horizontal coefficient
Nomogram for residual displacement
calculation

Residual displacement < Limit
30% cost reduction

Calculate the residual displacement

Fig. 2

Effect of reinforcement method
to prevent collapse

Effect of resin on preventing slip-out of stones

Without spraying

With spraying

No collapse even after
1000 gal of vibration

Displacement

2.0

Vehiclem

Soil nail1.65 mSlip-out of stacked stoneFig. 3

4

Seismic reinforcement design method for a masonryembankment type train platform

Masonry wall
Input acceleration

Displacement (mm)Seismic reinforcement method for a masonry embankment type train platform

Limit(Displacement
at top)

Input acceleration (gal)Fig. 1Time (sec)Results of shaking table test using a full-scale model of a masonry embankment type train platform

Annual Report 2020-2021

Railway Technical Research Institute

2. Anti-bridge-collapse device with seismic control function thatcan be installed in narrow spaces• An anti-bridge-collapse device having a seismic control function has been developed for railway
bridges with steel piers.
• It is a compact device that can be installed even in narrow spaces, to reduce the response displacement
during an earthquake by about 10 to 50% and prevent the bridge from collapsing when excessive
displacement occurs.
• The design method of the device has been developed including the mounting part, so it is immediately
applicable to actual bridges
Based on the fact that a road bridge with steel piers whose
upper and lower ends were pin-supported was collapsedin the Kumamoto earthquake in 2016, the prevention ofbridge collapse and the control of girder displacement are
required for railway bridges with a similar structure (Fig. 1).
Since many of these bridges are built over arterial roads
or railways, due to the limited clearance under the girders
and narrow spaces at girder bearings, existing countermeasures may not be able to prevent bridge collapse andcontrol girder displacement at the same time. Therefore,
as shown in Table 1, we have developed a device to satisfy
the following four requirements; (1) to prevent bridge collapse, (2) to control girder displacement, (3) not to obstruct
the space under the girders, and (4) possible to install innarrow spaces. As shown in Fig. 2, the developed device
has a structure in which multiple steel rods fixed to thesubstructure by post-installed anchors are integrated intoa frame and attached to the girder. The device is so small,about 0.4 m high with a base measuring 1.0 m x 0.4 m,that it can be installed in narrow places and places where
the clearance under the girder is limited. It also features aseismic control function that controls girder displacement
by absorbing energy thanks to the plastic deformation ofsteel rods, as well as a function that prevents the collapseof a bridge by controlling girder movement in the event ofexcessive displacement due to the high ductility of steelrods. By means of dynamic analysis simulating an actual
bridge, it has been confirmed that the girder displacement
after the failure of girder bearings can be reduced by about
10 to 50% (Fig. 3). A design method including the mounting
part has been developed for this approach, and the adoption to actual bridges has been decided upon.

Steel girder(1) Function to prevent bridge collapse
End cross beam・High ductility
・Support the weight of the girder

Steel pier

(1) Prevent bridge
collapse(2) Control girder
displacement(3) Does not obstruct the
space under the girder

Arterial road or railway

Fig. 1

Nonlinear characteristics of the device

About 1.0 m

Girder mounting rib

LoadIntegration frame(4) Possible to installin narrow spaces

Target bridge and performance requirements for the device
Abutment

Steel rod
Diameter: 50 to 100 mm

About
0.4 m
Substructure

Small size (3) Does not obstruct the space under the girder(4) Possible to install in narrow spaces

Steel pierSteel girder (21 m + 21 m)

Earthquake motion

Fig. 2

Displacement(2) Control of girder displacement
・Absorb energy(Seismic control function)

Overview of the developed device

Without device

With device

Limit

Approx. 40%
reduction

Time (sec)Fig. 3

Example of verification of effect by dynamic analysis

Annual Report 2020-2021

Performance (1) Prevent
requirements bridge
Construction
collapse
methodExpansion ofgirder seat

Existing method

Response displacement (cm)Developed device(2) Control(3) No(4) Possible togirderobstruction install in narrowdisplacement under the
spacesgirderDamperBearing
reinforcement

Developed device

Table 1

Performance comparison with existing methods

5

Major Results of Research and Development

3. High-accuracy estimation method for earthquake motion at
bedrock during large-scale earthquake• A method to estimate the earthquake motion at bedrock using seismic observation records at thesurface during large-scale earthquake has been proposed.
• It can be used to find out the cause of damage to structures, vehicles, and electric power poles after
an earthquake, and to shorten the restoration time.
• Compared with the conventional method, estimation error is reduced to 1/6 and calculation time to 1/30.
In order to evaluate the surface earthquake motions overa wide area, we first need to estimate the earthquake
motions at bedrock [Fig. 1 (b)] excluding the effect of thesurface layer, and then add the effect of the surface layer ineach area to them. The ground motion at surface of largescale earthquake [Fig. 1 (a)], however, is much affectedby the nonlinearity of the surface layer. Furthermore, theobservation record on the surface only reflects the local
soil character at the set-up point of the seismometer. It
is difficult to properly remove this effect and estimate theearthquake motions of bedrock with high accuracy using
conventional methods.
To solve this problem, we have proposed a method toestimate the earthquake motions of bedrock from motions
at surface with high accuracy by devising a way to search
for the solution in the convergent calculation. In the proposed method, single degree of freedom (SDOF) analyticalmodel is used in the initial stage of the calculation. A model
would be altered to the detailed analytical model with multidegree of freedom system after the result derived by thesimple SDOF model is converged to some extent, by which
faster calculation is achieved. As a result of estimating
the earthquake motions of bedrock using the proposed
method, the large amplitudes of motion, which are difficult
to estimate with the conventional method, can be well
reproduced. In addition, compared with the conventional
method, estimation error can be reduced to 1/6, while
shortening calculation time to 1/30 (Fig. 3).

(a) Ground surface observation record

Nonlinearizationof the ground

Pullback
calculation(To remove
nonlinear
behavior)

Bedrock(b) Earthquake
motion of bedrock(Unknown)

Fault

Fig. 1 Propagation of earthquake motion

By using the proposed method, we can improve the accuracy for estimated earthquake motions at the site withoutobservation record during a large-scale earthquake, andfind out the causes of damage to structures, vehicles,
and electric power poles in more detail. It can also be
utilized to improve the accuracy in the spatial distributionof ground motion in the Damage Information System for
Earthquake on Railway (DISER), which can be used toefficiently extract the points to inspect after an earthquakeand shorten the restoration time.

Estimation result

Earthquake motion of bedrockfor verification

Estimation accuracy
significantly improved

Acceleration (gal)

Proposed method

Estimation error (gal)Acceleration (gal)Conventional method(c) Damaged area

Conventional
methodProposedmethodCalculation time
1/30

Error
1/6

Calculation time (standardized by conventional method)Time (sec)Fig. 2

6

Estimation results of earthquake motion of bedrock

Fig. 3

Relationship between estimation error andcalculation time

Annual Report 2020-2021

Railway Technical Research Institute

4. Method to control snow accretion around a bogie using traveling wind
• For Shinkansen, a method to control snow accretion by combining an intake that draws in travelingwind and a nozzle that blows it onto bogies has been developed.
• From model experiment and numerical analysis, it was confirmed that the amount of snow accretionaround the body plate behind a bogie was reduced by 30 to 50%.
In recent years, Shinkansen lines have been extended tosnow-covered areas such as Hokkaido and Hokuriku, andcountermeasures against snow have become an issue inthese areas. Therefore, in order to improve safety withrespect to snow dropping from vehicles and to reduce thework load of removing snow accretion in snow-covered
areas in winter, we have developed a method to control
the flow of snow particles around the bogies and reduce
the snow accretion mainly around the body panels behinda bogie.
This method consists of an intake that draws in travelingwind from the side of a bogie and a nozzle that blows thedrawn-in air out onto the body panel behind the bogie(Fig. 1).
As a result of running tests using a model and snowfall
wind tunnel tests conducted to reproduce the advectionof snow particles onto bogies and snow accretion, we
found that the mass of snow accretion on the body panel
behind the bogie could be reduced by about 50% [Fig. 2(a)]. In addition, the results of a simulation using a shapethat was expected to control snow accretion in the modelexperiment, it was confirmed that the number of accreted
snow particles around the body panel behind a bogie was
reduced by about 30% [Fig. 2 (b)]. Furthermore, we investigated the effect of intake shape on aerodynamic soundusing a large low-noise wind tunnel, and confirmed that theincrease in noise due to the addition of countermeasuresagainst snow accretion could be controlled to less than
1 dB.
In the future, we will apply this method to actual vehicles
to verify the effect on preventing snow accretion and its
impact on the increase in noise.

Without countermeasures

Runn

ing d

irect

ion

IntakeNozzleFig. 1 Intake and nozzle layout

Annual Report 2020-2021

Number of accreted snow particles (x 104)

With this method(a) Snowfall wind tunnel tests
Reduced by 29%
Without
countermeasures

With this method
Running time(b) Snow accretion simulator

Fig. 2 Estimation of snow accretion control effect7

Major Results of Research and Development

5. Method for detecting obstacle on railway tracks using an onboard monocular camera• A method for detecting obstructions on railway tracks using a monocular camera mounted in the cab
has been developed.
• It was confirmed that the system can detect a person standing in daylight at a distance of 300 meterswith a probability of 98%.
• The system can be used to support the driver in detecting obstructions in front of the train.
When drivers detect an obstruction in front of the train,
they take measures such as stopping or slowing down. In
such cases, if automatic detection of obstacle in front of
the train were possible, the burden on the driver would be
reduced and safety would be enhanced.
In order to replace or support forward monitoring by visualobservation, we have developed a forward monitoring
method using an on-board monocular camera that targets
people on the platform or on the railway tracks. Unlike sign
markers, the person to be detected may differ in shape,
posture and clothing, so there is a limit to the detection
performance by general image processing methods. To
solve this problem, we used deep learning, which learns
the characteristics of people based on hundreds of thousands of human images to enable detection.
The developed system using a 4K camera was able todetect a standing person at a distance of 300 m in daylightwith a probability of over 98%, and a person at a distanceof up to 475 m could be detected (Figs. 1 and 2). It canalso be used to support the detection of obstacle in frontof the train, as an alarm can be issued within one second
after a person enters the railway tracks, whether standing
still or moving, and real-time detection is possible.

We will expand the range of detection targets, to include
a car on the railway track, sinking of railway track, earth
inflow, rockfalls, and fallen trees, and extend the detection
distance. In addition, we will develop the system to be able
to detect various events in front of the train irrespective of
day and night by using it in combination with ground-based
monitoring technology for sharp curves where visibility ispoor and with LiDAR at night. The system can also be
utilized for automatic train operation on general routes
with level crossings in the future.

Detection rate of more than 98% at ranges up to 300 m

Detection rate (%)

Example of detection result

Example of camera images

Person detected

475 m

Magnified view

Maximum distance 475 m

Distance to person (m)Fig. 18

Detection rate by detection distance for a person
standing in daylight.

Fig. 2

Example of a person detected by the system at a
distance of 475 m

Annual Report 2020-2021

Railway Technical Research Institute

6. Method to detect passengers approaching vehicles using
vehicle side cameras• A method to detect passengers, etc. approaching vehicles using cameras mounted on the sides ofvehicles has been developed, and it has been confirmed that no person is overlooked or misdetectedirrespective of day or night.
• It has also been confirmed that the maximum detection error of the passenger’s position on the platform
is about at most 20 cm, which is accurate enough not to overlook the person outside the Braille blocks.
• The system can also be used to support the driver in one-man operation.
we confirmed that no person was overlooked irrespectiveof day or night, that the maximum detection error of theperson’s position was about 20 cm, which is about a halfof that of Braille blocks with inward lines, and that it was
accurate enough not to overlook a person outside theblocks (Figs. 2 and 3).
This method can be utilized to support the safety confirmation by the driver in one-man operation, as well as for safety
confirmation on platforms in future automatic operation. We
will expand the detectable targets to include wheelchairs,
baby strollers, and running-in passengers and create asystem to ensure the safety of all on platforms.

Measurement error (cm)

Safety confirmation on the platform when a train departs
has been done visually by the train crews. In anticipationof the future shift to one-man operation even for a long setof trains, it is required to automate the safety confirmations
on platforms. Therefore, we have developed a method
to automatically detect the approach of passengers tovehicles by attaching cameras to the side of the vehicles
and analyzing the images from the cameras, and thus
confirm the safety of the platform when the train departs.
Four cameras are mounted at both ends of the vehicle,
front, rear, left and right, per vehicle. At the time of departure, the system detects persons by analyzing images fromtwo cameras on the platform side and estimates where
the person is standing on the platform by making stationequipment such as Braille blocks as markers and using
a coordinate transformation technique called projective
transformation (Fig. 1). If the estimated position of theperson is judged to be too close to the vehicle, the crews
are notified by sound and light. By processing 10 frames ofimages in a second, it is possible to give notification of any
approach in real time. As a result of an evaluation using
a prototype device incorporating the developed method,

Mean error
Maximum error

Distance from camera (m)Fig. 2 Measurement errors according to distance fromthe camera
Camera image

Image of platform floor

Vehicle-side
cameraProjective
transformation

Measurethe distanceArea requiring
attention

Mounted in cab

CautionFig. 1 Coordinate transformation of a person by projective
transformation

Annual Report 2020-2021

Notified bylight andsoundFig. 3 Testing detection conditions using a prototype
device.

9Major Results of Research and Development

7. Brake control method to compensate for reduced brakingforce• A brake control method that can be introduced in addition to the existing brake control system toimprove the stop position accuracy has been developed.
• The system automatically updates the brake command even when the braking force suddenly drops,
maintaining the accuracy of stopping at the target position.
• It can be utilized to assist the driver in improving the accuracy of the stop position and during automatictrain operation.
In the current braking system, the deceleration effect is
not constant even with the same brake command due toweather conditions and functioning conditions of regenerative brake, etc. So, the driver or the train automatic stop
control (TASC) adjusts the brake command to compensate
for the variation in deceleration effect and ensure the accuracy of the stop position. In response to this, we havedeveloped a control method that automatically updates thebrake commands, aiming to develop a braking system thatitself can detect the variation in the deceleration effect toimprove the accuracy of the stop position, without relying
on the driver’s operation or external devices.
For the developed control method, a new control device
is added to the existing brake control system, and when
this device receives a brake command or the data of
distance to the stop position, it updates the control brakecommand based on the speed fed back from the train andgives it to the system (Fig. 1). This allows the decelerationof the train to follow the target value calculated from theremaining distance to the stop position, and if the braking
force is insufficient, the control brake command is updated
sequentially (Fig. 2).

As a result of tests including forcibly reducing the braking
force while the train was running on the test line within the
RTRI, we confirmed that the developed control method wasable to control the extension of the braking distance andmaintain the accuracy of the stop position even when thebraking force was suddenly reduced (Fig. 3).
By adding this method to an existing brake control system,
it is possible to assist the driver’s operation to improve
the accuracy of train stop position. It can also be utilized
for automatic train operation to be realized in the future.

Control device
Brakecommand

Add to the softwareor install as a commandconverting device
Updated control brakecommandor
Distance tostop position

Train

Feedback
train speed

Existing braking system

Brake command(3) Update the notch sequentially
to follow the target value.

Control brake command(2) Increase the target
value to prevent
extension

Target value

Deceleration

Measuredvalue(1) Braking distance isextended by reduction of
force
Time

Fig. 2 Control operations of the developed method

10

Extended braking distance (m) under this condition
to reduce the braking force*

Fig. 1 System configuration of the developed method* Braking force reduction
conditions
Cut the train braking force by
25% below 20 km/h

Without control

With control

Fig. 3 Controlling braking distance extension when braking
force is reduced(Running test: initial speed 30 km/h)

Annual Report 2020-2021

Railway Technical Research Institute

8. Vehicle-mounted measuring device for gauge and twist
under loaded condition• A low-cost, vehicle-mounted device that can measure the dynamic values of gauge and twist underloaded condition, which often cause derailment accidents, has been developed.
• It can improve the running safety on service lines and in depots where track inspection cars are notused.
• By running tests, it has been confirmed that the variation in the measurement results is about thesame as that by track inspection car, and that the measurement results generally coincide with thoseby track inspection car. Unmanned measurement is also possible.
Because track inspection cars that measure track irregularity under loaded condition are expensive, hand-pushed
measuring device is often used to control static track irregularity without applying the load of vehicles. However,such measuring device cannot measure the track irregularity under loaded condition affected by the wheel load andlateral force when the vehicle is running, so significant
track irregularity may be overlooked. Therefore, we havedeveloped a measuring device that can be mounted on
maintenance cars and commercial vehicles to measuregauge and twist under loaded condition, which often cause
derailment accidents. This device is about 1/10 the priceof the previously developed on-board inspection device.
This device (Fig. 1) consists of a sensor unit that measures
track irregularity and a controller that processes the pulse
signals from the Doppler sensor to obtain the running
distance and speed. The sensor unit measures gauge,cross level, and twist by combining the output of the angle
sensor with the relative irregularity of the rail measured by
the two-dimensional sensor.
Through running tests, we have confirmed that the device
satisfies the reproducibility required for a conventional
track inspection car (error (=standard deviation of thedifference between the two measurements): gauge: 0.5mm, twist: 1.0 mm) and that the results are consistent
with those measured by the track inspection car (Fig. 2).

In addition, the detection error of measured position using
GNSS positioning results is less than 25 cm (Fig. 3), so
unmanned measurement is possible by utilizing the remotecontrol function.
This device enables measurement of dynamic trackgauge and twist on service lines and in depots, wheretrack inspection cars have not been used before, thereby
improving running safety.

Controller

Doppler sensor

Sensor unit

Doppler
sensor

1st time using thisdevice
2nd time using thisdeviceTrack-inspection car

Cross level

1st time using thisdevice
2nd time using thisdeviceTrack-inspection car

Twist

1st time using thisdevice
2nd time using thisdeviceTrack-inspection car

2 mm

Calculation of running distance, speed, etc.

Two-dimensional
sensorLaser beamController

Sensor unit

Fig. 1
Gauge

Angle sensor

Vehicle-mounted measuring device for gauge and twist
under loaded condition
Displayed on Google Earth

Turnout
10 mm

・Measurement time (around 10:00 - 16:00)
・At least three round trips on each line

The results of the round trip almost
overlapped, and the detection error
was less than 25 cm

5 mm

50 m

Positioning by GNSS

10 m
10 m

Alignment

Fig. 2 Measurement results

Annual Report 2020-2021

Fig. 3 High accuracy detection of measurement position by GNSS
(green line)

11

Major Results of Research and Development

COST REDUCTION

9. R
 estoration techniques for embankments reusing
collapsed soil
• A method of restoring embankments by reusing soil collapsed due to heavy rains, etc. has been
proposed.
• It takes advantage of the dehydration effect of lime and the increase in the strength of soil due tocompaction.
• The construction period and cost required for restoration of embankments can be reduced by 10 to
20% compared to conventional methods.
In recent years, there have been many cases where
embankments are damaged by heavy rains, etc. Whenan embankment collapses due to heavy rain, it is difficult
to compact the soil after the collapse again because it
contains a large amount of water. In addition, since the materials used to construct the old embankments did not meetthe current regulations in many cases, the soil needed tobe purchased for restoration, which posed a problem interms of construction period and cost. To address theseissues, we have proposed a method of restoring embankments by adding lime to the soil collapsed due to heavy
rains and compacting it for reuse.
This method utilizes (1) the dehydration effect of lime and(2) the increase in the soil strength due to compaction. As
for (1), because only the dehydration effect is expectedof the lime, an indoor mixing strength test is not necessary, and the required additive rate of lime can be easily
calculated by substituting the water content of the soil after
the addition of lime into the theoretical formula obtained(a) Dehydrate the collapsed soil by adding lime
Before adding lime

After adding lime

from the chemical reaction formula of lime and water [Fig.
1(a)]. As for (2), after dehydration with lime, construction iscarried out so that the degree of compaction becomes thetarget degree of compaction obtained from the required
strength using the relational expression between the
degree of compaction and strength [Fig. 1 (b)]. Since thisformula reflects the increase in soil strength due to the increased degree of compaction, even if the collapsed soil ismade of materials that do not meet the current regulations,
it can be reused by increasing the degree of compaction.
With this method, the construction period and cost required
for restoration of embankments after heavy rains and earthquakes can be reduced by about 10 to 20% compared toconventional methods. For a case where the embankment
collapses due to an earthquake, the collapsed soil can bereused by using only the relational expression between
the degree of compaction and strength shown in Fig. 1 (b).

(b) Implement compaction according to the required strength
Formulate the relationship between the degree ofcompaction Dc and the strength of the φpeak* Degree of saturation Sr: Value indicating thepercentage of water in the soil

* w0: Water content ofcollapsed soil
Calculate therequiredadditive rate
Additive rate of lime αw(%)

Fig. 1

12

Strength φpeak(degree)

Water content after additionof lime w(%)

Theoretical formula

Required strength

Degree of saturation
Sr=85%

Calculate thetarget degreeof compaction

Degree of compaction Dc(%)

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

Annual Report 2020-2021

Railway Technical Research Institute

10. Structural performance evaluation of existing bridges
by acceleration monitoring• An algorithm that can estimate deflection and reinforcing bar stress of a concrete bridge from its
acceleration monitoring waveform in less than one second with high accuracy has been developed.
• Quantitative evaluation of structural performance, such as train running performance and fatigue safety,
and determination of the need for repair or reinforcement can be performed without field measurementsthat require a great deal of labor.
without performing detailed cross-sectional calculations,by considering the increase in stress on reinforcing bars
due to cracks in the concrete (Fig. 1). This has made it
possible to evaluate the train running performance, suchas riding comfort due to deflection, and fatigue failure due
to reinforcing bar stress, from the measured acceleration waveform of the bridge. This method can estimate
the deflections and stresses with an estimation error ofless than 10% for an existing standard concrete girder
by high-speed processing of less than one second. The
validity of this method has been verified by comparing itwith numerical experiments and stress measurements on
actual bridges.
By applying this algorithm to acceleration monitoring, whichis small and can be installed permanently, it is possible
to evaluate the structural performance quantitatively anddetermine the need for repair or reinforcement of bridges
that, for example, resonate when a train passes by without
much labor (Fig. 2).

Method to convert to deflection
waveform

Acceleration monitoring waveform

Deflection(mm)

Acceleration(m/s2)

Error is large in normal
time integration

Time

Time
Fig. 1

Method to convert from the deflection waveformto the waveform of reinforcing bar stress
Waveform by developed method

Reinforcing
bar stress(N/mm2)

Some of Shinkansen bridges have been observed to havesignificant vibration due to resonance caused by passing
trains. The maintenance of existing bridges is generally
conducted only by visual inspection, while quantitative
data such as deflection and reinforcing bar stress are
periodically required to accurately evaluate structural performance such as train running performance and fatigue
safety, posing issues of increased labor and cost.
Therefore, we have developed a method to estimate thedeflection and reinforcing bar stress when a train passes
by with high accuracy, using the records of acceleration
sensors installed on the bridge. The accuracy of acceleration sensors is decreased in the low-frequency range
due to noises. To solve this problem, we have developed
an algorithm that replaces the low-frequency componentof the acceleration waveform with a waveform basedon vibration theory and integrates it into the deflection
waveform, as well as an algorithm that estimates the stresswaveform of reinforcing bars from the deflection waveform

Removal of conversion
error by this developed
methodActualmeasurements

Time

Method of integrating acceleration waveform into deflection and converting deflection waveform into stress on internal
reinforcing bars

Evaluation of structural
performance

Acceleration monitoring
Convert acceleration to deflection
and reinforcing bar stressStress on internal
reinforcing bar

Deflection

Example of temporary
installation of accelerometer(Fatigue failure, riding comfort, etc.)

Automatically transmit recorded data by radio(Data collection by visits is not required.)Fig. 2 Evaluation of structural performance of existing bridges by acceleration monitoring

Annual Report 2020-2021

13

Major Results of Research and Development

11. Multi-point synchronous measurement system for
railway bridge vibration using a video camera• A system that can synchronously measure the vibration of multiple arbitrary points on a railway bridge
using a video camera has been developed.
• The system uses videography to measure the dynamic deflection of railway bridges, the natural
frequencies of ancillary structures, and the micro displacements of bearings while trains are passingby, to evaluate their health efficiently.
In order to improve the efficiency and sophistication of
inspections using vibration measurement, which canevaluate the dynamic characteristics of structures with
objective numerical data, we have developed a multi-point
synchronized vibration measurement system using a 4K
video camera capable of high-speed imaging (Fig. 1),
and applied it to the inspection of railway bridges. The
displacement of a railway bridge caused by a runningtrain is calculated from the amount of change in the video
image and the ratio of the sizes between the actual size
and the image of the object being photographed. With thismethod, it is possible to measure vibrations at multiple
arbitrary points in the captured image synchronously bysimply shooting a video without setting up a target on theobject being measured.
If the camera is shaken by wind, etc. during shooting, anerror is caused. However, the proposed method automatically finds immovable points in the image such as bridge
abutments that do not move even when a train passes by,
and corrects the error caused by the vibration of camera byVideo camera

determining the relative vibration between the immovable
points and the point to be measured. In addition, the displacement by vibration can be obtained in actual amplitude
by determining the relationship between the gap and theactual size at any point in the image based on the result ofsimple measurement using the attached range finder. By
shooting a video of the railway bridge when a train passes
by from a riverbank, etc., the dynamic deflection and vibration shapes of the railway bridge can be measured (Fig.
2), and the deflection angle and bending moment of thebridge can be estimated from the differentiation of thedeflection. It can also efficiently measure multiple posts
for overhead contact lines, etc. at once. Moreover, it canmeasure horizontal, vertical, and rotational displacementsof the bearing with an accuracy of less than 0.1 mm and
0.1 degrees, respectively, which is difficult to measure
visually (Fig. 3). Periodical measurements will help to findthe deformation of bearings, such as damage and movable parts sticking. In this way, this system can achieve
sophisticated labor-saving inspections.

Running train

PC and logger

Fig. 2 Visualization of the dynamic behavior of a long span bridge
when a train passes by.

Range finder

Fig. 1 Multi-point synchronous vibration measurement system using a video camera

14

Fig. 3 Visualization of micro-behavior of bearings

Annual Report 2020-2021

Railway Technical Research Institute

12. Low-cost deterioration evaluation method for the backside
ground of slope protection works• A low-cost and short-time test method to evaluate the degree of deterioration of the backside groundof slope protection works has been developed.
• The stability of the slope can be determined according to the result of the above test.
• A maintenance manual for slope protection work has been prepared to outline the main points of theinspection method and countermeasure method.

Nd value(Resistance value of DPT)

Since there is a risk of collapse on slopes where the groundof the backside of the slope protection work is deteriorating, it is necessary to maintain the slope protection work
according to the degree of deterioration of the backside
ground. To address this issue, we have developed a lowcost and easy-to-use horizontal cone penetration test
method (Hereinafter referred to as HPT) to evaluate thedegree of deterioration of the backside ground and a slope
stability evaluation method using a nomogram.
In the HPT, a rod is driven horizontally through existing
weep holes in the slope protection work to the backside
ground to determine the Ne value (Resistance value ofHPT) (Fig. 1). Using the newly proposed conversion formula, this value can be converted to the Nd value of themethod for portable dynamic cone penetration test (JGS
1433-2012, Hereinafter referred to as DPT), which is widelyused in railways (Fig. 2), and the thickness of the degradedand sedimented part of the ground is determined (Fig. 3).
The test can be carried out in about 10 minutes for one
point without removing the slope protection work, which isabout 1/20th of the cost and 1/30th of the time required fora conventional boring investigation of the backside ground.
In addition, by using the newly created nomogram, thestability of the slope protection work can be determinedfrom the test results (Fig. 4).
We have compiled the results of these studies, examples ofhealth judgments to be utilized for visual inspections, andthe main points of countermeasure methods to create amaintenance manual for shotcrete slope protection works.
This manual also describes the simple elastic wave exploration, which was developed as an inspection method forrock slopes that are not suitable for penetration tests andcan be used as a comprehensive maintenance manual.

Theoretical formula for conversion
In-situ data

In-situ data regression formula
Ne Value(Resistance value of HPT)

Understanding the thicknessof the degraded part
Slope protection
work
Penetration distance (cm)Fig. 3 Example of HPT results

Annual Report 2020-2021

Fig. 2 Conversion of Ne values to Nd values

Slope gradient (°)

Nd Value(Resistance value of DPT)

Fig. 1 Horizontal cone penetration test

Unstable region
Plotting to evaluate
inspection results
Stable region

Judgment criteria

Thickness of degraded part (m)Fig. 4 Example of a nomogram for evaluating slope stability

15

Major Results of Research and Development

13. Boring method for constructing a crossing structure underrailway tracks making it possible to shorten the construction
period• A boring method for constructing a crossing structure under railway tracks that can control the amountof settlement of the track surface to the same level as before and shorten the construction period has
been proposed.
• The cost of measuring and managing tracks during boring and track maintenance can be reduced byapproximately 50%.
• A settlement prediction analysis method has been developed to be used to determine whether
countermeasures such as grouting are necessary.
Construction of tunnels, including roads that cross underrailway tracks, is underway to eliminate level crossings.
When constructing a tunnel crossing under railway tracks,
a temporary tunnel using square steel pipes is constructedin advance. With the conventional method, square steel
pipes are inserted one at a time for boring to create aclosed cross-section (Fig. 1), while track measurement andmanagement, track maintenance, and grouting are carriedout, so it requires a longer construction period and higher
cost compared to general tunnel construction.
Therefore, for constructing a temporary tunnel, we haveproposed a stepped boring method that can control theamount of track settlement at the same level as before(Fig. 2). The step-boring method is a method of boring thesecond square steel pipe simultaneously with a certain“separation” and a time difference from the preceding first
pipe. The appropriate amount of separation was clarified
by model experiment (Fig. 3). By boring two pipes at thesame time, the construction speed can be almost doublethat of boring one pipe at a time. This can reduce the timerequired for boring by half and the cost of track measurement and management and track maintenance duringboring by about 50%.
If the railway tracks and square steel pipes are in close
proximity to each other, it will be necessary to determine
whether countermeasures such as grouting to prevent
settlement are necessary. In order to do this accurately,
we have developed a numerical analysis method that can
precisely predict the amount of track settlement (Fig. 4).

If track settlement occurs,
maintain the track
TemporarytunnelRailway track andsquare steel pipe
are in close
proximity
Bore insideafter closing

16

Bore two pipes simultaneously with a certain
separation (= height of square steel pipe)

Overview of constructing crossing structure underrailway tracks

When one pipe isinserted at a time

The same amount ofsettlement can beachieved by separatingsquare steel pipes bymore than their height.
Separation L/Height D

Fig. 3

Conventionallyinserted one ata timeWhen two pipes
are inserted insuccession with aseparation

Fig. 2 Step-boring method

Track settlement (mm)Track settlement (mm)(When passing through the face)Fig. 1

Square steelpipe(Inserting)

Height D
Separation L

Relationship between separation and track settlement

Alert level

Analysis results Track
Measurement results maintenanceincludedConstruction cancellation level

Measurement
results of actual
construction

Grouting +track
maintenance

Boring area

Fig. 4

Verification of the numerical analysis results of track
settlement

Annual Report 2020-2021

Railway Technical Research Institute

14. Maintenance method for prestressed concrete sleepers according to the installation environment• A method to evaluate the soundness of prestressed concrete sleepers (PC sleepers) based on theirnatural frequencies and the degree of deformation caused by frost damage and salt injury has been
proposed.
• A maintenance flow and standard replacement criteria according to the installation environment havebeen proposed.
• These methods can be utilized for the efficient replacement and maintenance of PC sleepers.
For the maintenance of PC sleepers, visual inspection
is used to check the surface condition to determine theneed for replacement. In recent years, the number of PC
sleepers that have exceeded their designed service life of
50 years is increasing, so systematic maintenance basedon engineering evidence is required more than ever before.
Therefore, we have proposed a method to quantitatively
evaluate the soundness of PC sleepers, as well as a maintenance method according to the installation environment.
For the soundness judgment, we have proposed a method
to evaluate the soundness of PC sleepers based on theirnatural frequencies, which can be obtained by hammeringsound, and the degree of deformation caused by frost
damage and salt injury. For example, in the case of 3PRsleepers specified in JIS E1201, in an environment where
there is a possibility of frost damage, we found that thestandard thresholds for soundness judgment were thenatural frequency to be 750 to 800 Hz, and the area where

scaling (deterioration of concrete accompanied by flaking
and falling) occurred due to frost damage to be 40% of
the top surface of the PC sleeper. Furthermore, taking
the differences in the environment in which PC sleepers
are installed into consideration, we have proposed amaintenance flow by categorizing the areas into generalenvironment and areas with a high risk of frost damage
and salt injury (Fig. 1). In areas where scaling due to frost
damage (Fig. 2) is observed, the soundness of the sleepers
is determined based on scaling and natural frequencies.
For other frost damage, salt injury, and general environments, we have proposed replacement criteria based on
the cumulative passing tonnage as an index, indicatingthat the sleepers can no longer satisfy the JIS standard
values for bend tests (Table 1).
This enables maintenance according to the installation
environment and can be utilized to formulate a replacement
plan of PC sleepers.

ScalingNo

Possibility of frost
damageYesManage asafrost-damage environment

Less than 500 m
Manage asasalt-injury environment

Manage asageneral environment
BelowStandard passing
tonnage

Area with scaling

Over

Over

Plan to replace

Observe progress

Below

3PR (straight)

Plan to replace

Observe progress

Plan to replace■Replacement criteria for frost-damage and salt-injury environments

Below

Observe progress

Type
HammeringrodAbove thethreshold

PersonalcomputerMicrophone

Plan to replace
Observe progress

Hammering test

Fig. 1 PC sleeper maintenance flow

Annual Report 2020-2021

6PR (curve)
6PO (curve)Standard passing tonnage

Hammering test

■Replacement criteria in general environment
Standard passing tonnage (billion tons)
JIS-Sleeper
type
Average-baseLower limit-base
3PO (straight)

Over

ReplaceStandard passing
tonnage

Less than 40%

40% or more

Not
implemented
or below thethresholdFig. 2 PC sleeper with scaling damage

More than 500 m

Distance fromcoastline

Exposed prestressed concrete wire

3PR (straight)
3PO (straight)
6PR (curve)
6PO (curve)Standard passing tonnage (billion tons)

*
*
*
*

* These values are not applicable to the section where scaling isobserved in PC sleepers that do not use air-entrained concrete.

Table 1 Example of PC sleeper standard replacement
criteria

17

Major Results of Research and Development

15. Low-cost ballastless tracks for existing lines and roadbed
improvement method• A ballastless track for existing lines to fill the ballast layer with superfine-particle-cement fluid has beendeveloped. It is also applicable to a ballasted track that turned into a mud-pumping state by performing
partial ballast renewal.
• A post-filling roadbed improvement method that can be carried out with ballastless track construction
has been developed, and a design method for roadbed improvement that takes the amount of roadbed
settlement and fatigue failure of the grouted ballast layer into account has been proposed.
In order to reduce track maintenance costs, a construction
method to convert an existing ballasted track into a ballastless track with low cost is required. As a new elemental
technology to address this issue, we have developed the“ballastless track with superfine-particle-cement (SFC)
fluid,” which utilizes existing ballast, and the “post-filling
roadbed improvement method,” which can be laid on
relatively soft ground.
For the SFC grouted ballastless track, highly permeablesuperfine-particle-cement fluid, which can be grouted into
deteriorated ballast, is used. Normally, the existing ballast
is utilized, but if mud pumping occurs, the ballast is grouted
after partial ballast renewal (Fig. 1). With this method, theconstruction cost can be reduced by 40 to 50% comparedto the conventional grouted ballasted track that requires
renewal of the ballast for the entire track. Especially inmountain tunnel sections where the construction unit priceof ballast renewal is relatively high, conversion into a ballastless track with SFC fluid can be achieved at the same
cost as ballast renewal. Conversion into a ballastless trackwith SFC fluid is expected to reduce the maintenance costin sections where mud pumping of ballast frequently occurs
due to the mixture of fine particle soils.
The post-filling roadbed improvement method is to replace
the roadbed improvement layer to the new ballast first andthen fill the grout material to make the layer more solid later
for improving the roadbed to install the ballastless track
on soft ground. This can be carried out at the same timeas the ballastless track construction (Fig. 2). This makes
it possible to install a ballastless track on soft ground evenunder the condition of a short period of time available fortrack maintenance. With this method, the construction
cost can be reduced by about 60% compared to the conventional roadbed improvement method. In addition, we
have proposed a design method that takes the amountof roadbed settlement and fatigue failure of the grouted
ballast layer of a ballastless track into account.
These methods will be reflected in the “Guide to Repair
and Improvement Methods for Track and Roadbed on
Commercial Lines.”
Step 1: Replacement of roadbed material(1) In the case of no mud pumping

Tamping rammer

Use existing ballast
without ballast renewal
SFC fluid

Roadbed improvement layer(new ballast)

Grouted ballast layer with SFC fluid

Replace the soft roadbed in the
improvement area with new ballast* Existing ballast + SFC fluid

Grouting tube

Install a ballastless track on roadbed
improvement layer (new ballast)

Step 2: Grout filling(2) In the case where mud pumping occurred * New ballast + SFC fluid
Renew only the ballastthat turned into mud
pumping

Grout (post-filling)

Grouted ballast layer
with SFC fluid

Grouted ballast layer

Grouting tube

Cement stabilization layer of existing ballast or
Compaction layer of existing ballast

Fig. 1 Ballastless track with SFC fluid

18

Roadbed improvement layer
with grout material

Fig. 2 Post-filling roadbed improvement method

Annual Report 2020-2021

Railway Technical Research Institute

16. The image analysis engine for around track to support train
patrol• An analytical engine has been developed in order to automatically detect objects around the railway
tracks and changes in the wayside environment that may interfere with running trains, based on images
taken by cameras installed at the front of the train.
• The system can reduce the frequency of train patrols by accompanied persons in charge, and is used
as a system to support train patrols on commercial lines.
In order to comprehensively monitor the degradation
condition of the railway tracks and changes in the waysideenvironment, large amount of patrol works are conducted
on foot or from the train, therefore it is necessary to introduce a system to reduce the burden on persons for theworks while ensuring safety. Aiming to support train patrol
works, we have developed an analytical engine for images
around railway tracks that can automatically detect objects
and changes in the wayside environment that may interferewith running trains.
This analytical engine sets arbitrary spatial regions, suchas a clearance gauge, on the 3D data constructed fromimages taken by a stereo camera installed at the front of
the train, and diagnoses the presence of objects existinginside the region. In doing so, by estimating the alignment
from the rails in the image, it is possible to set up a spatial
region corresponding to the shape of curved line (Fig. 1).

With the difference detection technique to automaticallylink the images taken at two different times along the same
section and detect the differences between the two images,
it is possible to detect the appearance or disappearanceof objects on the wayside as well as changes in positionand shape (Fig. 2).
By automatically extracting areas that require attention
with this analytical engine, during train patrol services
conducted by persons in charge accompanied on the train,
it is possible to not only make diagnosis without dependingon the skill level of the person in charge and reduce thenumber of oversights, but also reduce the frequency of
train patrols by persons in charge. A part of this analyticalengine (confirmation of interference in clearance gauge)
has been introduced into the train patrol support system
of JR Kyushu, and has been in operation since April 2020.

Previous image
Taking images

Image from the front of the train

Interference determination

Current image (showing the differences)
Clearance gauge frame

Appearance of a person isdetected
Disappearance of an object is
detected

Changes in size and position of
the object are detected

Fig. 1 Example of interference confirmation by 3D
measurement

Annual Report 2020-2021

Point with difference detected

Fig. 2 Example of difference detection between images taken
at two different times

19Major Results of Research and Development

17. Insulator contamination estimation method based on
public data
• A method for estimating the degree of insulator contamination at any given point and time in an open
section, based on publicly available meteorological and topographical data, has been developed.
• It can be used to design equipment with contamination resistance according to the environment andto formulate maintenance methods.
If an insulator is contaminated by salt, etc. adhered on thesurface, the insulation performance is degraded, so theequipment should be designed and cleaned according to
the degree of contamination. Currently, the contaminationlevel is classified uniformly based on the distance fromthe coastline. If this can be subdivided, it is expected to
improve safety and reduce the labor required for maintenance. In order to do this, it is necessary to continuously
monitor the degree of insulator contamination at many
points, but a lot of labor is required to do this by actual
measurement.
Therefore, we have developed a method to estimate thedegree of insulator contamination at any given point andtime in an open section using meteorological data such as
wind speed, wind direction, and rainfall, and topographicaldata such as distance from the coastline (Fig. 1). Fig. 2
shows the results of estimating the degree of insulator
Convert to the wind speed at aheight above ground of 10 m

Topographical data

Distance from the coast at the
point to estimate

Enter topographical data

Calculate the amount of
insulator contamination perunit time

Enter calculation result

Meteorological data (from the nearest observation station to
the point to estimate)Wind direction

Wind speed

RainfallCalculate the salt density in
air per unit time

Surrounding environmental
application factor at the point toestimate

Enter meteorological datacontamination at the Anti-Salt Testing Station in Gatsugi of
RTRI (Murakami-shi, Niigata) over a one-year period, as
well as the measured values. For the degree of insulator
contamination under an occurrence frequency of 5%, which
is used when designing the contamination resistance ofequipment, the results based on the estimated values forthree years by this method and the measured values for thesame period were found to be in good agreement with eachother (Fig. 3). From these results, we have confirmed that
this method has a practical estimation accuracy. With thismethod, it is possible to design contamination resistanceand maintenance methods according to the environment,such as increasing the size and number of couplings ofinsulators at points with high levels of contamination, andextending the cleaning interval of insulators at points withlow levels of contamination.

Convert to the wind speed
at the point to estimate

Calculate the rate ofdecrease in the degree ofcontamination due to rain

Add to the degree of
insulator contamination of
the previous time

Estimated value of degreeof insulator contamination

Go to calculation of the next time

Fig. 1 Proposed algorithm

Degree of insulator contamination (mg/cm²)

Measured value

Occurrence frequency (%)

Value of occurrence frequency of 5%(Approximation of measured value)

Estimated value

Measured value

Estimated valueLightcontamination
Heavy
Super-heavy
Special& General Medium
contamination
contamination contamination
Degree & classification of insulator contamination

Date

Fig. 2

20

Value of occurrence
frequency of 5%(Estimated value)

Example of estimation results for the degree of contamination (on the coast of Japan Sea in Niigata)Fig. 3

Relationship between degree of insulator contamination and occurrence frequency

Annual Report 2020-2021

Railway Technical Research Institute

18. Degradation evaluation method for lithium-ion batteries
for vehicle control circuits• A degradation evaluation method has been developed in order to ensure the reliability of lithium-ion
batteries for vehicle control circuits and to reduce the labor required for inspections.
• Prediction of lifetime under actual use conditions is possible at the stage of vehicle design.
• Degradation diagnosis after starting vehicle operation can be available by adding filtering processaccording to the recorded vehicle data.
that they agreed with high accuracy (Fig. 1). We havealso proposed a degradation diagnosis method to easily
calculate the degree of degradation of batteries after thestart of operation based on charging/discharging data during vehicle operation, without performing measurements
during periodic inspections. As a result of comparing the
calculated values of the capacity index (differential capacity: value indicating the degree of degradation) using thismethod with the measured values obtained in the basic
experiment, we confirmed that they had a high correlation(Fig. 2).
The developed prediction method for the degree of degradation can be used to predict the battery lifetime according
to the actual operating environment in the design stage,in order to apply the same type of batteries to the control
circuit. The diagnosis of the degree of degradation after
the start of operation can be applied by adding filtering
process against the noise and various fluctuations in therecorded vehicle data.

Battery temperature

Battery in Chamber A(Actual operating
temperature +10°C)

Battery in Chamber B(Actual operating temperature +20°C)

Capacity

Plot: Actual measured value, Curve: Predicted value

Number of days elapsed

Fig. 1 Comparison of predicted values of capacity andmeasured values

Annual Report 2020-2021

Calculated value of capacity index (%)

Capacity (%)

Temperature (°C)

Lithium-ion batteries have been newly adopted as storage
batteries for the control circuits in some Shinkansen trains.
Compared to conventional lead-acid batteries, these batteries are lighter and are expected to have a longer lifetime.
In order to maintain the performance of the lithium-ion
battery over a long period of time and to eliminate theperformance evaluation work during periodic inspections,
we have developed a degradation evaluation method suitable for the operating environment in the control circuit ofa railway vehicle.
First, in order to obtain the degradation trend of the lithiumion battery, we conducted an accelerated degradation
test while simulating the temperature environment andcharging/discharging conditions in actual vehicles, andproposed a formula to predict the change from the initialstate to significant degradation while taking the fluctuationof the battery temperature into account. We compared thepredicted values of battery capacity, an important indicatorof degradation, with the measured values, and confirmed

Initial value (not degraded)

Progress of degradation(Correlation coefficient 0.98)

Measured value of capacity (%)

Fig. 2 Comparison of calculated values of capacity index
and measured values21

Major Results of Research and Development

HARMONY WITH THE ENVIRONMENT
19. Wheel tread management method based on wayside noise
prediction• A method for estimating the amount of local wearing depth of wheel treads from rail vibrations in
low-speed sections has been constructed.
• A method for predicting wayside noise when running high-speed sections based on the rail vibrations
during low-speed sections has been constructed, and the accuracy of the prediction has been confirmed
to be less than ±1 dB.
• This method can be utilized for wheel tread management by comparing the results of noise prediction
with environmental standards, etc.
When local wear (small hollows occurring in the circumferential direction with a length of about 300 mm) occurs
on the wheel treads of Shinkansen vehicles, the waysidenoise may increase, mainly due to an increase in bridge
noise. The amount of increase in wayside noise due to local
wear is greatly affected not only by the local wearing depthof individual wheels, but also by the location and numberof occurred local wears in the train set. In order to control
the wayside noise below a certain level, it is desirable tohave an efficient way to detect the wheel tread conditionsof the entire train set. Therefore, we have constructed amethod to estimate the local wearing depth of individual
wheels from rail vibrations in low-speed sections wheretrains run at speeds of 100 km/h or less, and then to predict
the wayside noise in high-speed sections where trains run
at speeds of 200 km/h or more.
Since the position where the part with local wear of thewheel contacts the rail changes each time, the value
detected by a single vibration sensor installed on the rail
fluctuates greatly each time. Therefore, we have developed
a method to accurately evaluate the effect of local wear onrail vibrations by combining rail vibration data from threepoints (Fig. 1). From the combined rail vibration, the local
wearing depth of individual wheels can be estimated. In
addition, we have extended the conventional method forpredicting the wayside noise of Shinkansens to developed
a new method to predict the wayside noise by reflecting thechange in rail vibration of each bogie when the train passes
through the low-speed section to the acoustic power levelof the bridge noise in the high-speed section by each bogie(Fig. 1). The difference between the predicted value andthe measured value of wayside noise was within about ±1
dB, demonstrating that it is possible to predict the noise
when running in high-speed section from the rail vibrations
when running in low-speed section (Fig. 2).
The results of the noise prediction in a high-speed section
with this method show the condition of the wheel treads ofthe entire train set, and it can be utilized for wheel tread
management such as the optimization of the wheel turning timing by comparing the results with environmental
standards, etc.

It is possible to predict with an accuracy of about ±1 dB
or less, considering the effects of local wear

Low-speed section(Measure rail vibration)

High-speed section

Predicted value (dB)Eachindicates theresult for one train

Vibration sensor

Bridge noise
Wheel withlocal wear

Combine the vibration data of three points
to evaluate the effect of local wear

Estimate the local wearing depth on individual wheels

Wheel tread management
for entire train set(Optimize wheel turning
timing)Compare with environmental
standards, etc.
Predict wayside noise

Fig. 1 Method for predicting wayside noise in a high-speed section fromrail vibration in a low-speed section

22

Measured value = Predicted value

Conversion

0 dB: Maximum of measured value

Measured value (dB)

Fig. 2 Relative level comparison of measured andpredicted values of wayside noises

Annual Report 2020-2021

Railway Technical Research Institute

IMPROVEMENT OF CONVENIENCE

20. High-accuracy train positioning system for tilting train• A train positioning system with a detection error of ±2 m that does not depend on Automatic Train
Protection (ATP) beacons has been developed.
• It can extend update period of on-board route data for about 10 years and save the labor for updating.
• The tilting system can be controlled according to the entry and exit in each curved section, to improve
the ride comfort.
Tilting train controls the carbody tilted angle according to
the position and speed to improve the speed in curves
and the ride comfort. In the case of current meter-gauged
railway lines, most of them identify the current position
based on the location of Automatic Train Protection (ATP)beacons, so it is necessary to constantly update on-board
position data for beacons. To improve this cumbersomeness, we have developed a train positioning system that
does not depend on the ATP beacon and does not require
the maintenance of on-board database for a long time.
In the developed method, the reference curvature data
generated by designated signal processing of the curvatureof the railway track measured during running in advanceis stored in the on-board database as route data. Thenthese data are compared with the curvature of the railway
track calculated from the vehicle’s yawing angular velocity
measured by the on-board gyro sensor and the runningspeed to detect the vehicle position (Fig. 1). In a runningtest, we have confirmed that this method can identify thevehicle position accurately with an error of ±2 m (Fig. 2).

A spatial filter, which is a length-based filter, is applied tothe calculation of the curvature of railway track to eliminate
the effects of differences in vehicle sway between different
types of vehicles and changes in track conditions due toaging. With this method, the update frequency of on-board
route data can be extended to about 10 years.
In addition, with the conventional method, there is a chance
to miss the vehicle position on the track if it travels on adifferent track from the database for passing other trains orin a large station. On the other hand, the developed method
can continue to detect the vehicle position maintaining an
accuracy of detection error of ±2 m, by detecting where
the difference between the data of curvature while runningand the reference curvature increases sharply to determine
the location of the track in the station.
With these methods, tilting trains can be controlled according to the entry and exit in each curved section while
reducing the update frequency of reference database of
curves thereby improving the ride comfort of vehicle.

Gyro sensor

Standard
deviation

Create runningcurvature data

Read map curvature
data

Route data

Running speed

Number of detections

Vehicle yawing angular velocity

Compare curvature data
Tachometer generator

Detection error in vehicle positioning (m)Vehicle position
recognition

Fig. 1 Train positioning system

Annual Report 2020-2021

200 fixed points x 4 runs (1 run: 88.5 km)

Fig. 2 Distribution of vehicle position detection errors

23

Major Results of Research and Development

21. Support system for countermeasures against delay basedon the impact of delays• A support system for countermeasures against delay has been developed to improve the punctualityof train operations and to increase the work efficiency to study measures to achieve this.
• The system extracts the priority locations for countermeasures that can improve punctuality of the rail
line, based on the number of sets of train and station likely to be affected by the delay at one location.
• By applying the methods to actual rail lines, the validity of the extracted locations and the improvementof punctuality after the timetable change have been confirmed.
Chronic delay of trains is one of the major causes of
reduction of passengers’ convenience. Aiming to improve
the punctuality of train operations and to increase work
efficiency for studying countermeasures to achieve this,we have developed a method to identify the sets of train
and station where measures to prevent delays, such as
the addition of running time or dwell time supplements,
should be prioritized.
In terms of countermeasures against delay, it is empiricallyknown that delays can be effectively reduced with fewer
countermeasures if priority is given to locations where
delays are likely to spread widely to other trains. Therefore,by newly defining the number of sets of train and station
where delays tend to spread as an index of the impact ofdelay, and identifying the area to which the delay at eachlocation spread based on the recorded data of daily delays,
we have constructed a new method to quantitatively evaluate the impact of delays. Next, we have devised a method
to extract the locations with a high degree of impact on
average over a certain period of time (e.g., one month) as
the priority locations for which countermeasures against

Color of train line
Train delay

0-minute delay
Impact: 2.6

Large
delay

Small
delay

2-minute delay
Impact: 59.9

Large
delay

Small
delay

Color of train line
Train delay

delay should be considered, and developed a support
system for countermeasures against delays implementing this method. This system makes it possible to study
and develop effective countermeasures against delays
in about one-fifth of the time required for a conventional
method, leading to simultaneously improve punctuality andconvenience and increase work efficiency.
As a result of applying this system to three actual rail lines,
we confirmed that the locations of high impact calculatedby the system were the same as the areas that the persons in charge of creating train timetables recognized theneed for countermeasures. In addition, we added totally
90 seconds of running time or dwell time supplements toeach intermediate station of the train with high impact,
and changed the departure track of the train at the starting station as countermeasures at the timetable change,
based on the locations extracted by the system. We foundthat the delays at and around the countermeasure points
were reduced, confirming the validity and usefulness of
this system (Fig. 1).

DelayspreadDelay
resolved

Delayspread3-minute delay
Impact: 84.8

Before revision
Delays occurring at a high-impact location
spread to many trains in a wide area.

Delay
resolved

0-minute delay
Impact: 8.8

After revision

Countermeasures against delay were taken in a high-impact location andthe delays in the location of countermeasures and their affected area were significantly reduced.

Fig. 1 Application and validation of the method by evaluating the impact of delay for actual rail lines

24

Annual Report 2020-2021

BASIC RESEARCH

Railway Technical Research Institute22. Numerical simulation of ventilation in a vehicle byopening windows while running• A numerical simulation tool to predict the amount of ventilation by opening windows has been developed.
• Based on the simulation results, a simple prediction formula for the amount of ventilation by opening
windows has been proposed.
• The simple prediction formula for the amount of ventilation inside a vehicle can be used to evaluate
the measures against “closed space” for railway vehicles.
One of the countermeasures against infectious diseases
in commuter vehicles is to ventilate a vehicle by opening
windows as one of the “closed space” countermeasures,
and a quantitative evaluation of the ventilation amountis required. To address this issue, we have developed
a numerical simulation tool that can predict the amountof ventilation in a vehicle by opening windows while running. In addition, by organizing the results of predicting
the ventilation amount under various conditions, we haveproposed a simple formula for predicting the ventilation
amount.
The simulation of ventilation in a vehicle has been realized
by introducing a function to evaluate ventilation amountto the previously developed fluid analysis tool “Airflow
Simulator” (Fig. 1). The simulation calculates the air flow
outside and inside the vehicle at the same time. In doing
so, the ventilation amount is evaluated by tracking the

air flow using virtual particles generated continuously at
114 locations in the vehicle. The window opening height,
seating arrangement, passenger position, etc. are variable,
allowing the prediction of the ventilation amount under any
given condition. We have confirmed that the simulation
results are generally consistent with on-track test results.
The simulations revealed that the ventilation amount is
proportional to the train speed and window opening area
and that there is no significant difference depending on theoccupancy rate or seating arrangement when the window
opening height is less than half-open. Based on this knowledge, we have also proposed a simple prediction formulafor ventilation amount (Fig. 2). This prediction formula canbe used to evaluate the measures against “closed space”
for railway vehicles, such as setting the amount of window
opening according to the window shape.

Flow velocity (m/s)
Running direction

Simple prediction formula
Ventilation amount = 0.026 x train speed x window opening area (total)

Air flow in vehicle(Occupancy rate: 100%, speed: 72 km/h)

Ventilation amount (³/s)(Note) Although prediction accuracy varies depending on the
aspect ratio of the window, this formula can be applied to any
car classification when the windows are less than half-open.

Window opening height (cm)Virtual particle

Track air flow using virtual particles

Fig. 1 Simulation to predict ventilation amount

Annual Report 2020-2021

Train speed: 70 km/h
Window: 1.2 m wide x 6 windows

For an in-vehicle
volume of 114 m³

When the window opening height is 10 cm,
the air is replaced in five to six minutes.

Fig. 2 Proposed simple prediction formula and example ofits use25

Major Results of Research and Development

23.Analytical evaluation method for the speed at which
hunting occurs• The relationship between the initial amplitude and the running speed at which hunting occurs has
been clarified.
• The speed at which hunting occurs can be efficiently evaluated based on the specifications including
the wheel profile
The phenomenon where a bogie suddenly starts lateral movements at a certain speed, which develop into large amplitude
oscillation, is called “hunting.” Since hunting has a significant
impact on the running safety of vehicles traveling at high speed,
the speed at which hunting occurs is confirmed to be sufficiently
higher than the operating speed using a rolling stock test plant
during the development of new vehicles. However, it is known
that the speed at which hunting occurs varies depending on
how the initial amplitude of lateral displacement is given on
the wheelset in the test. This relationship between the initial
amplitude and the speed at which hunting occurs is called thecritical hunting curve (Fig. 1). The relationship between these
two has not been theoretically clarified before. In this study,
we have constructed an analytical method for calculating thecritical hunting curve based on the specification of the vehicle
and the wheel tread profile.
Fig. 1 shows that an oscillation diverges to the hunting above
the critical hunting curve, and one converges below it. It also
shows theoretically there could exist a steady state oscillationon the critical hunting curve that neither converges nor diverges.
This indicates that the steady state oscillation is unstable, whichmeans that the oscillation will diverge or converge by evenslight disturbance (Fig. 2). Therefore, we have constructed amethod to calculate the amplitude of steady-state oscillation
corresponding to a point on the critical hunting curve by applying
a nonlinear calculation method to the vehicle dynamics model.
In this method, we can efficiently obtain a solution by using
the characteristics of a critical hunting curve that decreases
monotonically as a whole when the running speed increases.
With this method, it is possible to analytically evaluate the speed
at which hunting occurs according to the initial amplitude of
the wheelset.
In addition, we have clarified that the speed range in which
hunting occurs expands when the nonlinearity increases, as in
the case of arc wheel profile where the tread gradient changes
depending on the contact position with the rail, compared to theconical wheel profile having constant tread gradient (Fig. 3).

Speed range at which hunting occurs

Time

Running speed

Fig. 1 Critical hunting curve

Time
Diverge clockwise

Lateral displacement of wheelset

Arc wheel profile
The outer circumference of the spiral matches the inner circumference of the spiral.

Critical hunting curve

Running speed

Below the critical hunting curve
Converged

Lateral speed of wheelset

Lateral displacement of wheelset
Amplitude of steady-state oscillation

Lateral displacement of wheelset

Conical wheel profile

In the case of above thecritical hunting curve

Lateral speed of wheelset

Diverge

Lateral speed of wheelset

Converge clockwise

Converge

Running speed

Fig. 3 Example of critical hunting curve calculation

26

Wheelset
Lateral displacement

Boundary between
convergence and divergence
Critical hunting curve

In the case of below thecritical hunting curve

Wheelset
Lateral displacement

Amplitude of lateral displacementon wheelset

Saturation amplitude after divergence (of hunting)Directly on the critical hunting curve
Steady-state oscillation without
convergence or divergence

Lateral displacement of wheelset

Above the critical hunting curve
Diverge to develop into hunting

Fig. 2 Steady-state oscillation on the critical hunting curve

Annual Report 2020-2021

Railway Technical Research Institute

24. Evaluation of the influence of wheel machining markson the friction coefficient by visualizing the contact area
• A technique to visualize the state of contact between wheel and rail using ultrasonic waves has beendeveloped.
• It has been clarified that the friction coefficient between wheel and rail is affected by the degree ofgap between the contact areas.
The higher the critical derailment coefficient is, the bigger
the margin against vehicle derailment occurs. In order toobtain a high critical derailment coefficient, it is important tokeep the friction coefficient between wheel flange and raillow. Since some derailments have occurred just after wheel
machining, the friction coefficient between wheel and rail
is considered to become higher immediately after wheel
machining, but the influence of wheel machining on thefriction coefficient has not been fully clarified. Therefore,
we investigated the influence of wheel machining on thefriction coefficient, by visualizing the contact area between
wheel and rail just after wheel machining using ultrasonicwaves (Fig. 1).
As a result of the friction test simulating sliding contact
between the wheel flange and the rail just after wheel
machining, we found that the friction coefficient increased
with the increase in number of rollings. Therefore, wehave developed a technique to visualize the contact area

between wheel and rail in the process where the friction
coefficient increases to check the changes in the contact
state. As a result, the surface irregularities (wheel machining marks) were deformed by the 20th rolling, and the
profile of the contact area tended to change rapidly (Fig.
2). Comparing the change in the friction coefficient withthe degree of gap between the contact areas obtainedby analyzing the visualization results, we found that thefriction coefficient increased as the surface irregularities
deformed and the contact areas became closer together(decreasing the degree of gap) (Fig. 3).
As the metal surfaces directly adhere to each other until
some contaminants adhere to the wheel surface after
wheel machining, the friction coefficient is expected to be
kept high as shown in Fig. 3. Therefore, lubricating thewheel flange just after wheel machining is considered tobe effective in reducing the friction coefficient.

Non-closed contact
Mileage (m)Wheel

Number of rollings: 2nd time

6th time
Surface irregularity
8th time

Friction coefficient

Rail
Friction coefficientincreases

Contact areas
become closer

Degree of gap (μm)

4th time

20th time

80th time
Image of surface irregularity of awheel just after wheel machining

120th time
Image of the change ofcontact state with rolls

Fig. 1 Image of surface irregularity
and contact state just after wheel
machining

Annual Report 2020-2021

Distribution of contact pressure

Fig. 2 Changes of contactpressure between wheel flange
and rail

Number of rollings
Closed contact

Fig. 3 Relationship between numberof rollings and friction coefficient/degree of gap

27

Major Results of Research and Development

25. W
 heel slide protection simulator to supplement ontrack testing• A wheel slide protection (hereafter, WSP) simulator that combines actual pneumatic piping for braking
and a real-time computer has been developed.
• By confirming the performance of the WSP algorithm in advance, the number of test runs for on-track
testing can be halved
Adjustment of the control algorithm through on-track
testing is the most important process in evaluating theperformance of WSP under braking. However, it is not easy
to stably reproduce the low adhesion conditions between
wheels and rails in on-track testing, and the algorithm must
be adjusted under different adhesion conditions for eachrun. To solve this problem, we have developed a WSP
simulator that combines the advantages of real machineand computer.
The simulator consists of a real machine part that uses
the same pneumatic system components as actual rail
vehicles, from air reservoirs to brake cylinders, and acomputer part that specifies the adhesion conditions,vehicle model, and WSP algorithm (Figs. 1 and 2). The
real machine part uses variable length pneumatic piping
to make the response of the pneumatic system variable.
The computer part calculates the speed information (vehicle and circumferential speed) in real time based on

the specified adhesion conditions (Fig. 3) and the vehicle
model. This forms a real-time loop, that is, when the speed
information satisfies the slide detection conditions of the
WSP algorithm, the WSP dump value in the real machinepart works to discharge the air in the brake cylinder, and thecomputer part reads the pressure change and calculates
the speed information again.
With this simulator, various verifications can be performedby arbitrarily specifying the adhesion conditions and WSP
algorithm, while reproducing the response characteristics
using the pneumatic system of the actual rail vehicle.
For example, by comparing the distribution of stopping
distances for combinations of various adhesion conditionsand WSP algorithms, the tendency of each WSP algorithm
can be statistically evaluated (Fig. 4). Thus, by comparing
the performance of promising WSP algorithms prior to theon-track testing, the number of test runs for the on-track
testing can be halved.

Air reservoir

Electro-pneumaticchange valve

Piping
WSP dump valve
BrakecylinderAir reservoir
Electro-pneumatic changevalve
WSP dump
valve

Real
machineBrake command
WSP dump valve
operation command

Measurable
Operation flow
Pneumatic piping

WSP algorithm

Variable lengthRealmachine part

Computer part

Brake cylinder

Tangential force coefficientFig. 1 Overview of WSP simulator

Brake cylinder pressure

Brake equipment

Computer
Adhesion conditions
between wheels
and rails
Tangential force
coefficient
Vehicle model
Vehicle andcircumferential speed
Frictionmaterial
Pressing
Brakingcharacteristics force
force

Fig. 2 Functional block diagram of WSP simulator

Control algorithm A

Frequency

Control algorithm B

e

nc

ista
gd
kin (m)Bra
Slip ratio (%)

Fig. 3 Specifying low-adhesion conditions

28

Stopping distance (m)Fig. 4 Stopping distance distribution of WSP algorithm

Annual Report 2020-2021

Railway Technical Research Institute

26. Non-contact method to detect rail openings using
aerial ultrasonic waves• A non-contact method to detect rail openings using aerial ultrasonic sensors has been devised.
• A running test at speeds up to 80 km/h was performed using a testing car equipped with the proposed
method to confirm that the ultrasonic intensity at the opening part of rail was lower than that at the
middle part.
• A method to analyze rolling contact between a wheel and a rail has been developed to evaluate thethermal stress and plastic deformation, which is necessary to clarify causes of deterioration such as
hollow wear and wheel flats.
• By comparative verification with braking test results, it was confirmed that the rise in wheel temperature
could be reproduced within an error of 10%.
Currently, rail breakages are detected along with the train
position by the track circuit, but if a wireless train control
system is introduced, a separate method of detecting
rail breakage is required. Therefore, we have devised amethod to detect rail openings based on the intensity ofultrasonic waves that were transmitted to the rails fromthe vehicle and received by an aerial ultrasonic sensor
without contact (Fig. 1).
When ultrasonic waves are input from the sensor to therail through the air, the transmittance is extremely low,
about 1/10,000th of the transmittance when the sensor
is in direct contact with the rail. For this reason, we haveadopted a system that can transmit and receive ultrasonicwaves more powerful than those used in general ultrasonic
testing, and that can strongly amplify the strength of thereceived waves. In addition, in order to minimize the attenuation of ultrasonic waves due to sensor separation andvibration, we used a relatively low-frequency band of 100
Normal section

kHz among ultrasonic waves. In order to facilitate the judgment, we have also proposed a method to distinguish fromrail joints and a method to convert the received ultrasonicwave intensity into a time history waveform and display it.
In addition, we conducted laboratory test and confirmedthat the system could detect even when the rail ends werein contact with each other, which cannot be detected withtrack circuits.
As the result of a running test conducted on a test line using
a testing car equipped with this system at speeds up to 80
km/h, we have confirmed that the system can transmit andreceive ultrasonic waves to and from the rail and that theultrasonic wave intensity decreases at the rail openings(Figs. 2 and 3). At present, we still have some issues, suchas the inclusion of noise in the received waveform whena squeal noise is generated on a curve. We will continue
to improve the system and aim to put it to practical use.

Rail breakage point

Constant input of ultrasonic waves

Transmitting
sensorUse the principle that theultrasonic waves
transmitted from vehicle torail without contact areblocked at rail openings.

Receiving sensor

Breakage

Received ultrasonic waveform

Reception level is lowered at the breakage point.

Fig. 1 Image of proposed method
Rail joint Rail opening

Transmitting sensor

Rail joint

Rail opening

Ultrasonicwave intensity

Receiving sensor
Threshold* A method to distinguish between rail joints and openings
has also been proposed separately (patent pending).

Ultrasonic wave

Fig. 2 Testing car equipped with sensor

Annual Report 2020-2021

Fig. 3 Waveform when passing through a rail opening29

Major Results of Research and Development

27. C larification of wear mechanism of current collecting
materials caused by frictional heat
• It has been clarified that there are four modes of mechanical wear of current collecting materials
depending on the contact temperature, and that softening of the material is an important factor.
• A numerical analysis model that can calculate the temperature of the contact point due to frictional
heat has been constructed.
Current collecting materials such as contact wires andpantograph contact strips are subject to wear due tovarious factors such as contact force, running speed andcurrent. The effect of Joule heat by current has alreadybeen made clear, but the mechanical wear mechanism
caused by sliding has not been fully clarified.
Therefore, we have developed a new wear-testing machinethat can measure the contact temperature between thecopper plate and the contact strip during sliding and investigated the relationship between the contact temperatureand the wear mode caused by frictional heat. As a result,
we found that there were four modes of mechanical wear
depending on the maximum and average temperatures,and that the softening of the material due to the temperature was an important factor in the transition of the wear
mode (Fig. 1). In particular, we confirmed that the surfaceof the contact wire was significantly worn, like delamination

Wear Mode Ⅱ

Wear Mode IV

in wear form II, which is considered to be the cause ofsignificant wear of the contact wire in low-speed sections
where the frictional heat is small and the average contact
temperature does not rise.
Furthermore, we constructed a numerical analysis model(Fig. 2) that could calculate the temperature rise at thecontact point by inputting the frictional heat as heat flux
to the contact area, and confirmed by simulation that themeasured average temperature of the contact point wasconsistent with the analyzed value (Fig. 3). In the future,
we plan to facilitate the technology development to reducethe maintenance cost of current collecting materials, byintegrating this analysis with the analysis of temperaturerise due to Joule heat that we have developed so far,to develop a method for predicting the wear mode thatwill occur in the actual field, and to propose measures tocontrol wear by controlling the wear modes.

Contact wireContact
wire

Soften point on
contact strip
Approx. 400°C

Both contact wireand contact strip aresoftened.

Contact strip

Contact wire issoftenedintermittently.

Contact wireis always
softened.

Neither contact wire
nor contact strip aresoftened.

Soften point ofcopperApprox. 200°C

Average contact temperature (°C)
Wear Mode I

Contact
strip

Wear Mode III

Fig. 2 Contact temperature analysis model and analysis
exampleAverage contact temperature (°C)

Maximum contact temperature (°C)

Frictional
heat

Measured value

Analyzed value

Frictional heat (W)Fig. 1 Conditions for development of wear modes andwear surface on copper plate for each wear mode

30

Fig. 3 Measured and analyzed values of contact
temperature

Annual Report 2020-2021

Railway Technical Research Institute28. Method to evaluate physical properties of frictional
materials by numerical simulation• A method to calculate the physical properties of frictional materials by numerical simulation has been
constructed based on the created analytical model using CT images of frictional materials to reflect
their microscopic structures.
• The constructed method was applied to pantograph contact strip materials to reproduce their physicalproperties.
• It can be utilized to estimate physical properties before prototyping and to clarify the phenomena on
a microscopic scale that cannot be measured.
Frictional materials for railway, including pantograph contact strips and brake pads, are made of several materials.
The physical properties and behavior of such materials
vary depending on the microscopic structure, such as the
combination, proportion, and arrangement of the containedmaterials. Therefore, it is difficult to estimate the properties with high accuracy by conventional physical property
estimation methods based only on material proportions,and it has been necessary to repeatedly make prototypes
and measure the physical properties when developing newmaterials. To solve this problem, we have constructed ananalytical model that reflects the microscopic structureusing X-ray CT images, and constructed a method to calculate the material properties accurately by FEM (Fig. 1).
By applying the constructed method to a copper-impregnatedcarbon-based material, which is one of the pantographcontact strip materials, we have confirmed that the method
can reproduce the physical properties more accurately
than the conventional estimation method (Fig. 2). In
addition, the obtained analytical model can be used to
calculate the distribution of stresses and temperatures ona microscopic scale (Fig. 3).
This method makes it possible to estimate the physicalproperties of a material before it is prototyped, so we can
expect to improve the efficiency of material development.
Since we can also estimate the stress and the temperature
distributions on a microscopic scale, which is difficult tomeasure, we can utilize them to clarify the phenomena ofwear and damage.

100 μm ~ 1 mm

Elastic modulus,
thermal conductivity, and electricalresistivity, etc.

3D image by X-ray CT

Segmented image by
constituent material

FEM model

Physical properties byhomogenization analysis

Fig. 1 Overview of the modeling of microscopic structure and physical properties evaluation method for materials

Electrical resistivity (μΩm)

Thermal conductivity (W/mK)

Slidingsurface

Proposed
ActualConventional
method measurement
methodProposed
Actual Conventional
method measurement method(Error bars show maximum
and minimum values.)(Error bars show maximum
and minimum values.)

Fig. 2 Comparison of calculated and measured physicalproperties of pantograph contact strip materials

Annual Report 2020-2021(a) StressFig. 3(b) Temperature

Example of analysis of microscopic state distribution inside a pantograph contact strip material

31

Major Results of Research and Development

29. Detection of changes in drivers’ physical and mental
state using physiological indicators• Physiological indicators based on Heart rate and respiration that are suitable for estimating the physicaland mental state of the driver have been proposed.
• As Heart rate and respiration are relatively easy to measure, they are suitable for measuring on site.
• A comprehensive judgment method to optimize multiple indicators for each individual has been devised
to improve the estimation accuracy.
In order to maintain safe train operations, it is important thatthe driver, who works alone, is always in good physical andmental condition. Therefore, we have proposed a methodfor detecting the states that may affect safe operation,such as over-tension like panic and arousal reduction, fromphysiological data. Realizing this detection method makes
it possible to provide appropriate support for the driver toreturn to good condition.
It has been considered difficult to apply physiological
data for safety support which requires a high degree of
accuracy, due to the large differences among individuals.
Therefore, we have proposed physiological indicators
using Heart rate and respiration to estimate the activity ofthe parasympathetic nervous system of autonomic nerve,
which is activated during relaxation and suppressed during
tension. Conventional indicators require 15 to 30 seconds
for measurement, but the proposed indicators can becalculated in every respiratory cycle (about 3 seconds).
The non-tension indicators appear more frequently thanconventional indicators in arousal level 1, and they appear

less in arousal levels 4 and 5, indicating higher estimation
accuracy (Fig. 1). Measurement of respiration also has theadvantage of providing information that reflects mental
and physical state, such as startle reaction and sighing.
In addition, we have devised a method to automaticallyselect effective indicators suitable for each individual fromfive indicators including the proposed indicators (Table1), and determine both tension (including psychologicalagitation) and non-tension (including arousal reduction)with high accuracy using the selected effective indicators(comprehensive judgment method). As shown in Fig. 2,
there are problems with the rate of appearance of correct
judgments and the number of false positives in the judgments of individual indicators (in the purple box), but the
judgments using the comprehensive judgment method (inthe pink box) have both a high rate of appearance and a
low rate of false positives for both tension and non-tension,indicating that judgments can be made with a high degreeof accuracy.

Table 1 Examples of selected effective indicators for each individual (10 persons)

Arousal level (subjective evaluation)
1. Feeling sleepy
2. Feeling a little sleepy
3. Normal (fit for driving)
4. Feeling a little nervous or upset
5. Feeling nervous or upset

Participant ID

Five indicators

Proposed indicator

Conventional indicator
Proposed indicatorHeart rate interval
Respiratory length
Heart rate regularity
Respiratory regularity

Appearance rate (%): Judged to be tension

Proposed indicator

Fig. 1 Arousal level and nontension judgment

32: Effective for both tension and non-tension judgments

Respiratory length Heart rate regularity Respiratory regularity

Judged to be non-tension

Appearance rate (%)

Subjective evaluation

Heart rate interval: Judged to be non-tension

Judged to be tension

Non-tension Tension Non-tension Tension Non-tension Tension Non-tension Tension Non-tension Tension

Fig. 2Comprehensive
judgment

Non-tension Tension

Results of judgments by each indicator in tension and non-tension situations

Annual Report 2020-2021

Railway Technical Research Institute

Annual Report 2020-2021

33

News
Release
1. Digitalization Technology Innovation Project Started
On April 1, the Railway Technical Research Institute set up the “Digitalization Technology Innovation
Project.”

1. Purpose of setting up the project
The new master plan of RTRI, RESEARCH 2025, haslisted “Innovation of railway systems by digitalizing
technologies” as one of its basic policies. In order toproceed with the research and development for thispurpose quickly and in a cross-cutting way, RTRI set
up and started the “Digitalization Technology Innovation
Project”. Under this project, all the research staff will work
together to promote digitalization of railway systems byconducting basic research into image analysis and artificial
intelligence and obtaining the research and development
outcomes with the latest digitalization technologies as
quickly as possible.
2. Research activities under this project(1) To propose measures to innovate railway operation
using digitalization technologies
Identifying new technical challenges that will contributeto innovating railway systems using state-of-the-art
digitalization technologies.

34(2) Research and development to address new technicalchallenges
Promoting research and development on the abovementioned technical challenges and basic research into
artificial intelligence and image analysis by using stateof-the-art digitalization technologies.
(3) Integrated and cross-cutting research and development
Conducting cross-cutting research and development
through coordinating different fields and topics that sharethe same digital techniques.
(4) Development of the know-how and resources forresearch
Identifying sharable technologies, promoting cross-cutting
use of them and providing researchers training programs
The following figure shows specific research topics
regarding digitalization that RTRI has been addressing. In
the fiscal year 2020, 40 research projects will be started,
aiming to introduce image analysis, artificial intelligence,

Annual Report 2020-2021

Railway Technical Research Institutetelecommunications and networking technologies torailway operations.

that will provide us a shared base for cross-sectional
commitment.

【Image analysis】
We will develop techniques using image analysis
technologies to detect obstacles lying 600 meters aheadof a train and to visualize objects that are difficult torecognize by human eyes and seek to apply them torailway operation in order to improve the safety andinnovate maintenance of railways.

Some examples of the 40 research topics are as follows.

Specifically we will develop a system to detect on-thetrack obstacles by analyzing images ahead of and aroundthe train, a system to support visual monitoring of the track
and condition-based monitoring systems for vehicles,
structures, and tracks.
【Artificial intelligence】
We will develop methods to automatically extract machinelearning data which is useful for detecting troubles ofrailway facilities and a method to trace train control anddecision making. We will seek to apply the outcomes todevelop automated detection, prediction and decision
making on drivers and facilities conditions.
Our research and development includes a method todetect troubles of tracks and facilities early and to estimate vehicle equipment conditions using the digital
data measured on board, and a method to estimate
conditions of train drivers based on physiological indicators. In addition, basic technologies including a method ofearly earthquake warnings using machine learning dataof seismic waveforms will be developed.
【Telecommunications and networking】
We will address research to apply 5G mobile communication systems and millimeter waves to safety related
control. This technology is expected to support autonomous train operation which will be more resilient, withless ground equipment.
We will develop autonomous train operation systems
that control the trackside equipment during train
running based on digital information on track andtrackside conditions, disaster prevention and maintenance and passenger flow. We will also develop
energy-conserving technologies such as coordinatedpower control. We will also develop a data platform

Annual Report 2020-2021

Autonomous train control method
We will build an autonomous train control system. In
this system, running trains directly control the trackside
equipment such as turnout point machines and levelcrossings and autonomously set the routes and speed
profiles. With this system, ground equipment will be
reduced and driverless train operation will be achieved.
Early trouble detection method for track and facilities
with on-board digital sensing data
This method will be able to detect early troubles
and failures of tracks and facilities including track
subsidence and bridge pier subsidence and slant onlywith on-board sensing data. This technology will prevent
train running disruptions and accidents and enable
more efficient maintenance by planned repair andreinforcement.
Improved trouble detection method for overhead line
fittings
Using the images obtained by on-board line cameras, thismethod will detect troubles of the fittings such as pull-off
arms and crossing bars at crossovers by the informationof their shapes and colors. This will save labor in theoverhead line maintenance and achieve more efficientfacility maintenance.
Early earthquake detection method with machinelearning of big data
We will propose a method to apply machine learning datato train operation in real time in order to raise performanceof early earthquake warnings. Using a massive amountof waveform data measured by seismometers, thismachine learning will make it possible to distinguishbetween noises caused by earthquake shakings andtrain vibrations and to estimate earthquake elements.
Automation of the machine learning process introuble detection
We will aim to apply to commercial train operation asystem to detect troubles by applying machine learning
to vibration data of vehicle equipment. For this35

News Releasepurpose, we will develop a method to automaticallyextract machine learning data, for example, to remove
abnormal data from among normal data.
We will conduct research and development of theabove-mentioned technologies by utilizing basic
technologies such as image analysis, artificial intelligence, telecommunications and networking in across-sectional manner and coordinating related research
projects. Based on the outcomes, we will support
railway operators in applying digitalization technologies
to railway operations.

3. Organization of the project
The project promotion committee including the core committee will determine the research policies and, basedon the policy, each of the research divisions and centerswill conduct the research. The Image Analysis and IT
Laboratory that was set up in the Signaling andTransport Information Technology Division in 2017 willbe the core for utilizing digitalization technologies anddeveloping the research staff.

The members of the core committee are as follows:
Project Manager :Dr. Norimichi Kumagai, President
Vice Project Manager :Dr. Ikuo Watanabe, Executive Vice President responsible for the entire research and development
Dr. Shunichi Kubo, Executive Director responsible for the Research and Development Promotion Division
Working Leader :Mr. Shigeto Hiraguri, Deputy General Director, Research and Development Promotion Division
Members:
5 division directors, 5 laboratory heads and 3 senior researchers

36

Annual Report 2020-2021

Railway Technical Research Institute

4. Discussions at the first meeting
The Digitalization Technology Innovation Project was set
up on April 1 this year and its first meeting was held
online on April 28, with a limited number of 10 attendants(Photos 1 and 2).
The meeting was started by President Kumagai’s opening
note and followed by discussions on the basic policies
and the work schedule for this project. The issues
discussed there included specific research targets
including application of digitalizing technologies to maintenance, coordinated control of power supply network andtrouble detection by data. They also discussed theimportance of coordinated research among different
fields through sharing algorithms, sharing expertiseand outcomes, and collaboration with other organizations including overseas ones in order to introduce
state-of-the-art technology speedily. In addition, it was
proposed that we should conduct on-line research
into the latest technologies under this restriction of all theactivities due to COVID-19. President Kumagai concluded
that sharing and integrating information among different
research fields are the key of this project.

【Keynote Message by President Kumagai】
We will be utilizing digitalization technologies quickly intrain operation and solving the issues of safety, maintenance, energy and cost. In order to do so efficiently,
the role of this project is to share information and show
guidelines through integrating relating research topics andprojects. The core members and responsible researchers must cooperate and promote this work together. As
our research collaboration partner SNCF also has astrong interest in the railway application of digitalization
technologies, I would like to proceed with the research
together, inspiring each other. I would like to ask for yourcooperation and expect all of you to do vigorous research
activities.
【Comment by Executive Vice President Watanabe】
In this age of rapid technological advancement, it isimportant to proceed with this project swiftly. As railway
operators have high expectations for the introduction ofdigitalization technologies, we would like to extensively
mobilize our resources covering a variety of technicalfields in order to pursue this project.

Online meeting with President Kumagai(right) and committee members

Annual Report 2020-2021

37

News Release

2. RTRI Develops Presstressd Concrete Girder Crack
Detection System with Conductive Coating Material
RTRI developed Presstressd Concrete (PC) Girder Crack Detection System with Conductive Coating
Material enabling early detection of the cracks and their location on PC girders.

【Overview】● Fine cracks occurring on PC girders are important
clues to assess the performance of the girders but
difficult to detect because they are generated only
when trains are passing. This system is able to findsuch fine cracks by measuring the changes in electric
circuit resistance caused by the damage of the conductive
coating material applied onto the undersurface of thegirders.(Fig.1)● The measured resistance can be checked at a distantplaces via internet.(Fig.2)

Fig.1 Crack detection with conducting coating materialFig.2 Crack detection system applied onto PC girders

38

Annual Report 2020-2021

Railway Technical Research Institute【Advantages of this system】
This system will enable early detection of the occurrence
and locations of the cracks that are generated on theundersurface of PC girders and are difficult to find by
visual inspections. With this system, railway operators
will be able to find such fine cracks by measuring
the changes in electric circuit resistance caused by
the damage of the conductive coating material applied
onto the undersurface of the girders. It leads to more
effective repair and reinforcement of the girders andmaintenance schedule.
【Background】
The PC girders that have been used for 20 to 50-meter
length bridge girders are using PC steel bars. A tensile
force applied to the PC steel bars beforehand generates,
as a reactive force, a compressive force acting in thelongitudinal direction of rails, and it prevents crack
generation even under the impacts of train passing.
However, the compressive force is diminished if the
PC steel is ruptured due to corrosion and other reasonsand cracks might be generated on the undersurface ofthe girders.
At the initial stage, only a small number of PC steel
bars have ruptures and cracks occur only when train
passes. But the cracks start to occur constantly with
the increase as the number of ruptured bars increase,the cracks occur constantly, and PC girders might
have seriously damage. Although it is effective for PC
girder maintenance to detect the cracks at the initial stage,
they are difficult to detect in visual inspections.

【Details of this system】
In this system, electric circuits are built on theundersurface of the PC girders by spraying the
conductive coating material. If the surface coated withthe material is disrupted by cracks, the circuits are cut
off and cracks can be detected.
The circuits consists of a crack detecting section andconducting section. The concrete of the crack detecting
section is directly coated with the conducting material inorder to detect the number and locations of cracks. On
the conducting section, elastic urethane tapes are placed
and the tapes are coated with the conducting coating
material. Therefore this section is less likely to be cut off
even if cracks occur. (Fig.3) If the occurrence of cracks
need to be identified in a certain area of the girder, thedetecting section is placed and for other areas, theconducting section. By arranging both types ofsections like this, multiple cracks and their locations
can be detected as is shown in Fig.4. In addition,by analyzing the relationships between the numbers
and locations of cracks and the bearing force of the PC
girders, it will be possible to estimate the remaining
bearing force at the time of crack occurrence.
The measuring device of this system runs on electric
batteries for more than 1 year. Since the data of circuit
resistance measured 50 times per second can be recorded
onto SD cards or other media and is electronicallytransmitted, it is possible to confirm the data quickly via
internet at distant places (Fig.2)

Fig.3 Detecting section and conducting section

Annual Report 2020-2021

39

News Release

Fig.4 Circuit configuration on the undersurface of a PC girder

Fig.5 Feature of cracks generated on the coating material
applied to detecting section and conducting section

With indoor testing, it has been confirmed that the
conducting section circuit is not cut off even if 1mmwide cracks occur, while the detecting section circuit iscut off by 0.2 mm-wide cracks. (Fig. 5)
Crack generating tests have been conducted on thebridge-model test specimen and it has been confirmed
that, when cracks that occur only under train running
are generated, only the detecting section circuit iscut off but the conducting section circuit is not andthus the cracks can be detected.

40【Test operation on commercial lines】
This system has already been installed to a conventional line facility for a test purpose. It has been
more than 3 years since it was applied to theoutside facility in December 2016 but the system has
been working without any troubles nor maintenance work
except battery replacement.
Part of this research has been implemented with therailway technical development grant by the Ministry ofLand, Infrastructure, Transport and Tourism.

Annual Report 2020-2021

Railway Technical Research Institute

3. RTRI's initiatives in "Research 2025" for addressing SDGs
The Railway Technical Research Institute started to address Sustainable Development Goals (SDGs).
1. Objective
Through the research activities based on the master plan
“RESEARCH 2025”, RTRI will contribute to achieving 9
goals among the 17 SDGs, focusing on “Goal 9: Industry,Innovation and Infrastructure” where RTRI has advantage
and enhance its presence as a research institute.
2. What RTRI will do
Our society is facing a number of issues including protection of the global environment, increasing social burden
due to the aging populations and regional disparity of
economy. Radical technological innovation is essential
in order to overcome these issues. As a research entity
leading technological innovation for railways, RTRI will
provide solutions to the difficult problems facing railways
in coordination with railway operators, academic and research institutes and industries, and pursue the researchand development to achieve a sustainable society and tocreate the future for railways.

Figure 1:

Annual Report 2020-2021

Through the research activities based on “Masterplan –Research and Development Creating the Futureof Railways - RESEARCH 2025”, RTRI will address
the activities to achieve 9 goals among the 17 SDGs
including “Goal 7: Affordable and Clean Energy” and“Goal 8: Decent Work and Economic Growth” placinga particular emphasis on “Goal 9: Industry, Innovation
and Infrastructure”. (Fig. 1) Our specific activities areshown in Table 1.
We will communicate our contribution (Fig. 2) in ourperiodicals and website.

9 SDGs RTRI is addressing

41

News Release

Table 1:42

Research activities to achieve SDGs under “RESEARCH 2025”

Annual Report 2020-2021

Railway Technical Research Institute

Annual Report 2020-2021

43

News Release

Building resilient railway systems against increasingly severe weather disaster

RTRI supports SDGs

Labor saving by digitalized maintenance

Autonomous train operation
Figure 2:

44

R&D to achieve SDGs

Annual Report 2020-2021

Railway Technical Research Institute

4. New Executive Board Members of RTRI
On June 12, The Railway Technical Research Institute appointed new executives at its 35th meeting of
councillors. Accordingly, Eisuke Masada who has served as Chairman since April 2007, Norimichi
Kumagai, President since June 2013, Kiyoshi Sawai, Executive Vice President since June 2013, Yoshihiro
Suda, part-time executive director since April 2007 and Mitutoshi Inami, full-time Auditor since July
2011 resigned.
Masao Mukaidono, Tomoyuki Koishi and Atsushi Furukawa were newly appointed to full-time executive
director, Hiroyuki Ohsaki to part-time executive director and Kiyoshi Sawai to full-time Auditor.
Current board members approved on the 47th meeting of the board members following the councillors meeting are
as follows:
Chairman 		Representative Director Masao Mukaidono
RTRI			full-time 			(New appointment)
President 		Representative Director Ikuo Watanabe		
RTRI
			full-time 		(Promotion)
Executive Vice President Representative Director Kimitoshi Ashiya		
RTRI
			full-time			(Promotion)
Executive Vice President Representative Director Shunichi Kubo 		
RTRI
			full-time 			 (Promotion)
Executive Director
full-time 			Shunya Shiozaki 		
RTRI
Executive Director
full-time 			Tomoyuki Koishi 		
RTRI						(New appointment)
Executive Director
full-time 			Atsushi Furukawa
RTRI						(New appointment)
Executive Director
part-time 		
Masanobu Tabata
Hokkaido Railway Company
Executive Director
part-time 		
Tomomichi Ota 		
East Japan Railway Company
Executive Director
part-time 		Toshio Otake 		Central Japan Railway Company
Executive Director
part-time 		Yasushi Neki 		West Japan Railway Company
Executive Director
part-time 		Eiichi Yata 		
Shikoku Railway Company
Executive Director
part-time 		Youji Furumiya 		
Kyushu Railway Company
Executive Director
part-time 		Yasushi Kamata 		
Japan Freight Railway Company
Executive Director
part-time 		
Mami Aoki 		Doshisha University
Executive Director
part-time 		
Yoshifumi Nomura
Japan Private Railway Association
Executive Director
part-time 		
Hiroyuki Ohsaki 		Graduate School of the University of Tokyo						(New appointment)
Auditor 			full-time 			Kiyoshi Sawai 		RTRI						(New appointment)
Auditor 			part-time 		
Tatsuhiko Yamada
Central Japan Railway Company
Auditor 			part-time 		Fumiyasu Wakahara
Certified Public Accountant
On the 47th meeting of the board members, it was approved that Norimichi Kumagai and Mitsutoshi Inami are
appointed to Senior Advisor and Eisuke Masada was honored with a Fellow of RTRI.

Annual Report 2020-2021

45

News Release

5. Messages from Former and Current Chairmen andPresidents of RTRI
On June 12, The former and current presidents and chairmen of RTRI delivered following messages
to all the employees.
(1) Message from the former chairman Eisuke Masada
Since 2007, I have served as chairman of RTRI for 13
years, fulfilling the task of obtaining authorization of
RTRI as a public interest corporation by government andhosting WCRR2019. Today I pass my responsibility as
chairman to Prof. Masao Mukaidono. During these 13
years, although the world experienced the Lehman Shock,the transport revenue of JR companies steadily increased
and we have been able to add some more large-scaleequipment to our testing facilities. We have promotedcooperation with overseas research organizations andhave been sending a larger number of researchers tothese organizations. In addition, the activities and presence of RTRI’s Railway International Standards Center
have been firmly established and the center has beenrecognized as an international standardizing organization.
RTRI set and implemented two master plans, RESEARCH
2010 and RESEARCH 2020. Over these years, RTRI
contributed to the development of railway technologies
even after the first wave of railways speed increase
ended. We have established simulation technologies
for railways and developed operation and power supply
control and maintenance technologies based upon the
rapidly advancing information network technology. In
the meantime, RTRI has established its reputation as
an impartial railway research body through the efforts torestore the damaged areas after the Great East Japan
Earthquake in 2011 and a lot of heavy rain disasters
and to develop new disaster prevention technologies.
I believe this has been achieved by continuous effortsof the management people and all the staff of RTRI anddeeply appreciate them.
I have to leave office amid this uncertain, unforeseeable
situation due to the Covid-19 pandemic. At this moment,
we cannot clearly imagine what shape the world take after
Covid-19. But I believe, with RTRI’s efficient and flexible
response and expertise, we will be able to overcome theradical social change. I hope further development of RTRI

46

and even more energetic research efforts of all the staff
members at RTRI.
(2) Message from the new chairman Masao Mukaidono
Since I have served as councilor of RTRI for many years,
I know very well what RTRI’s researchersare doing andI hope to contribute to RTRI’s further development. I
am deeply interested in safety and security and havebeen proposing to build “safety science” that analyzes
safety through the integrated research into the aspectsof technologies, organization and human resources. Now
I am so pleased to get involved in the raiway safety andsafety culture.
RTRI has been building trust in the society throughestablishing advanced technologies as one and only specialized railway technical research organization. We havethe responsibility to continue, maintain and develop what
has been built so far. In the prospectus of RTRI, it reads“RTRI develops advanced, broad range technologies
covering basic ones to practical appliations, accurately
responds to the need of the society and cotributes to thedevelopment of science and culture of Japan.” We haveto continue this contribution to the society with unitedefforts and with pride. The Covid-19 pandemic has drastically changed the world and pushed us towards a newera. No organizations will be able to survive withoutchanging their ways of working. RTRI also has to open
a new trail. However, a crisis can be transformed into
an opportunity and this is the time to share our wisdom.
We need to go forward into a new society.
Although the society is always changing, I believe weshould not change our basic principles.
Keeping the respect for the basic principles and ideals,
we should read the changing times and the future andrespond to them flexibly. Business entities should respect
three categories of safety. First one is the safety of customers, that is, product safety in the field of manufacturing
and in the world of rail operation, railways’ safety itself.

Annual Report 2020-2021

Railway Technical Research InstituteThe second is the safety of employees that means labor
safety ensuring safe, healthy and happy lives for employees. The third is the safety of the corporate entity whichmeans a sustainability achieved through complying withlaws and orders, fulfilling corporate social responsibility
and gaining trust of the society. My reponsibility at RTRI
is to overview RTRI’s activities from a different, broader

Former chairman Masada(3) Message from the former president Norimichi
Kumagai
Since I became president of RTRI in 2013, the third year
as a public interest corporation, I was keenly aware of
the necessity to drive the growth of RTRI as a public
interest corporation. This objective took shape as themaster plan RESEARCH 2020 and RESEARCH 2025
outlining the direction that RTRI should take in the next
30 years. In addition, a new research division and alaboratory were added during these years. I am pleased
that these division and laboratory have been expanding
their activities and producing a lot of research outcomes.
It was particularly memorable that “Vision” of RTRI was
set and our “Mission” and “Strategy” were clearly defined.
Throughout these years, I have been addressing “reconfirming RTRI’s unwavering role,” “emphasizing safety
technology, speed-increasing technology and basic
research including simulation technology development,”
and “fully utilizing RTRI’s broad range, crosscutting
research capabilities based on the expertise achieved
over the long history as a research institute.” I have alsostressed, as the base of all the activities, “maintaining thetrust of the national government and railway operatorsand providing high-quality research outcomes quickly
and flexibly.” Setting a research map and readdressing

Annual Report 2020-2021

and longer perspective and to create, with all the board
members and staff members, a comfortable workplace
where all the employees can work happily and with highmotivation.

New Chairman Mukaidono

basic research were also important tasks. Now I am
confident that the research and development at RTRI hasreached a highly advanced level with the dedication andmotivation of all the employees.
The new president Watanabe has a multilateral perspective, excellent communication skills and tireless energy
and is absolutely the right person for this position to drive
the sustainable development of RTRI. I am very muchlooking forward to his leadership in the years ahead.
Researchers need to seek original inspiration and enhance their research potentials at the same time. In
order to solve issues, they have to work together as ateam as well as pursuing each one’s originality, andthis is the crosscutting, collective power of RTRI. Now,
we are in the midst of unstable, uncertain reality beyond
our expectation, but it is a favorable aspect of this circumstance to be able to arm ourselves with digitalization
technologies that will create new railway systems. I hopethat the researchers at RTRI will always keep in mind that
we need to gain trust of rail operators and the society, face
the threatening situation with the wisdom and potentials
as a team and overcome challenges. I am looking forward
to your renewed inspiration and best efforts under theleadership of the new chairman and president.

47

News Release(4) Message from the new President Watanabe
I would like to express sincere thanks to the former chairman Prof. Masada who has been providing us valuableadvice on the management and R&D over years and theformer president Dr. Kumagai who has been managing
RTRI under the powerful leadership.
Thanks to thededication of Prof. Masada and Dr. Kumagai, RTRI has
been implementing the research and development as an
institute trusted by the government and railway operatorsand enhancing its presence.
Currently, Japanese and global rail industries are facing an unprecedented predicament due to Covid-19
pandemic.
However, this year, 2020 is the starting
year of the master plan RESEARCH 2025 and we willcontinue our activities without any changes according to
the principles described in the plan, “Enhancing safetywith an emphasis on improving resilience to natural
disasters,” “Developing innovative railway systems
based on digital technologies,” “Creating high-quality
research outcomes by pursuing excellence across all
fields of activities,” “Enhancing international presenceof the Japanese railway technologies,” “Creating a work
environment to help the employees develop their fullpotential and undertake challenging tasks”. We will
steadily continue the research and development aimed
at improving safety, cost reduction, environmental
protection and improving convenience in order to enhancethe value of railways and to provide customers safe andcomfortable rail transport services again.
I would like to fulfill the responsibility of managing RTRI,
keeping in mind the following three policies.

Former president Kumagai

48

Firstly, we should keep an adventurous spirit to takeon challenges. While digitalization technologies havebeen showing a spectacular progress, the circumstances
around railways are dramatically changing in terms ofpassengers’ behaviors and supply chains businesspractices. Therefore, we may have to advance into newresearch fields and we may not be able to produce usefuloutcomes just by following our conventional waysof thinking and doing research. I hope that RTRI’s
researchers will be always ready to change things to bechanged and address new research fields and challenging tasks.
Secondly, we need to raise the basic research ability ofeach researcher including the insight to understand theessence of problems or various phenomena, problem
solving ability based on scientific and engineering approaches. For this purpose, we will put emphasison basic research that is important to train researchers’
fundamental research capability. Since RTRI needs to
conduct investigations into disasters and troubles of railway facilities quickly and to propose effective measures,
human development and construction of testing facilities
for these activities will be continued.
The third task is to implement RESEARCH 2025. I will
be doing this task, adding necessary changes to the plan
according to the circumstances.
I am always trying to “think positively and enjoy working”.
As research and development is to create new values
that nobody has come up with before, it’s fun. Now is thetime for us to enjoy our everyday work and keep going
forward together.

New President Watanabe

Annual Report 2020-2021

Railway Technical Research Institute

6. RTRI Develops On-Board Measurement Method to Identify
Resonant Bridges
Railway Technical Research Institute developed a method to identify resonant bridges that vibrate
intensely due to resonance using the data measured by running trains. This method has enabled tosave the labor required for ground measurement and made possible more efficient inspections.

[Overview]
Usually, while a Shinkansen train passes on a bridge,
downward displacement occurs due to the moving train
load. In the meantime, due to the aging of bridges andincreased train speeds, some of the bridges resonate
with the vibrations of the running train and the bridge

Fig.1

vibrations increase with train passing. [Fig. 1(b)] Since
large amplitude
resonance affects ride comfort anddurability of facilities, preventive measures may beneeded. Therefore, it has been necessary to identify
bridges that resonate with passing trains (resonant
bridges) among a vast number of bridges.

Vibration waveforms of ordinary (non-resonant) bridges and resonant bridges

In order to solve this problem, RTRI developed a new
method marked by the following two points:
・The vibration data measured on the passing train includes
vibrational components specific to the resonant bridges.
We have been able to identify resonant bridges by focusing
the differences between these vibrational components
measured by the first car and the last one. (Fig.2) Prior
to the development of this method, it has been necessary

Annual Report 2020-2021

to mount one sensor to each bridge and to measure thevibration amplitude of each bridge from the ground. The
newly-developed method has made it possible to measure
the entire area that a train runs through and therefore theworkload can be greatly reduced.
・This method can be easily introduced to bridge
inspections by using the data of the existing track
maintenance database system (LABOCS).

49

News Release

Fig. 2The method to identify bridges that vibrates intensely using on-board measurement[Using the method for effective maintenance]
This method has already been used on the Shinkansen
track and 10 resonant bridges have been identified. The
results help determine the priority in inspecting bridge
facilities and enhance the efficiency of maintenance.
Going forward, it is expected as well that this method will
be used to evaluate impacts of train speed increase on
bridges and their facilities and to determine the necessityof introducing any measures.

*1 ResonanceResonance occurs when difference between thefrequencies of bridge vibrations cause by a passing train
and the natural frequency of the bridge gets smaller.
*2 LABOCS
A software that analyzes and processes a wide variety ofdata including the track irregularity and vehicle vibrations.
This software was developed by RTRI and used by 6 JR
passenger rail companies and other railway operators forthe track condition monitoring and maintenance.

50

Annual Report 2020-2021

Railway Technical Research Institute

7. RTRI’s Researcher Receives the Young Scientist Award
Dr. Hirofumi Tanaka, Senior Researcher of RTRI, received the Young Scientist Award for the fiscal
year 2020 one of the Commendation prizes by the Minister of Education, Culture, Sports, Scienceand Technology. On August 25, Dr. Tanaka was handed the commendation certificate by Dr. Masao
Mukaidono, Chairman of RTRI.
Award winner:Dr. Hirofumi Tanaka
Senior Researcher, Track Geometry and Maintenance,
Track Technology Division
The award-winning research:
Improving the efficiency of rail roughness maintenance[Outline of the research]
On rail surface, roughness is generated at the time of
manufacturing or by train running. When a train runs on
the rough-surface rail, significant noises and vibrations
might be generated depending on the extent of roughness.
Since the wavelengths and amplitudes of the noises andvibrations are minute, several to hundreds millimeters
and less than 1 millimeter respectively, the techniques
to detect and measure the roughness efficiently andprecisely have not yet been established so far.
Then, Dr. Tanaka conducted the following:
1. Development of the method to evaluate the roughness
efficiently using the noise and acceleration data measured
on board.
2. Development and commercial application of the groundequipment that is capable of measuring the roughness
accurately
3. Proposing an efficient roughness controlling method
using the above method and equipment

The result of this research enables quantitative
management of rail-surface roughness, and cost reduction
and improvement of railway facilities maintenance.
[Comment by Dr. Tanaka]
I am greatly honored to receive this prestigious award. I
would like to express sincere thanks to the people at RTRI
and other organizations including railway operators who
have given to me a lot of guidance and advice.
The subtle roughness on the rail surface generates noises
and vibrations and they might damage the tracksideenvironment and the ride comfort in the cabin, andfurther on, damage facilities. The results of this research
will contribute to achieving efficient rail-roughness
maintenance to prevent the growth of roughness and tofurther development of railways.
With this encouraging award, I would like to continue theresearch in order to contribute to further development ofrailways and creation of a happier society.
* Young Scientist Award by the Minister of Education,
Culture, Sports, Science and Technology:
This award was launched in order to support the nextgeneration researchers’ career formation and to contributeto developing original and creative technologies and isgiven to young researchers who have shown outstanding
research abilities by conducting the research into newlyemerging issues or from original perspectives.

Dr. Tanaka (left) handed the award certificate by Prof. Mukaidono

Annual Report 2020-2021

51

News Release

8. RTRI’s Researcher awarded the Medal with Yellow Ribbon
Dr. Fumiaki Uehan, Director of Railway Dynamics Division at RTRI, was awarded the Medal with Yellow
Ribbon in the spring in 2020.
Since the award ceremony was canceled due to the novel coronavirus outbreaks, Dr. Uehan was handed
the award certificate and the medal by Prof. Masao Mukaidono, Chairman of RTRI on August 25 at RTRI.
Award winner:
Dr. Fumiaki Uehan
Director, Railway Dynamics Division
The award-winning research:
Invention of the non-contact vibration measurement
system for structures diagnosis[Outline of the research]
Since Japan has been facing many issues such asincreasingly severe natural disasters, aging social
infrastructure and decreasing working-age population, it
is an urgent necessity to develop labor-saving technologies
for structures inspection and diagnosis. Dr. Uehan
developed the non-contact vibration measurement system“U-Doppler” and made it available for commercial services.
U-Doppler is capable of detecting the structures damage
caused by disasters and deterioration by aging. Since thissystem features a correcting mechanism that compemsates
for the effects of winds and ground vibraions, it is capableof measuring subtle vibrations of structures accurately
and conducting quantitative structures inspections by
irradiating the structures with a lazer from outside remoteplaces.
In addition, a drone has been used for inspections sincean earlier stage and it has made it possible to detect local

Dr. Uehan (left) handed the award certificate
by Prof. Mukaidono

52changes on structures and trackside slopes.
Since these techniques enable quantitative inspections
without requiring the work at high places or in the areas
close to tracks, they have been widely used mainly for
railways.
[Comment by Dr. Uehan]
I am greatly honored and pleased to receive this prestigious
Medal with Yellow Ribbon and I am so grateful that thisaward has shed light on the research and development in
the field of structures inspection.
I have been focusing on improving the technologies forrailway structures inspections for about 20 years. I would
like to express sincere thanks to the people at RTRI andaffiliated companies for their cooperation in developing
and raising the popularity of the U-Doppler and to the staff
people of railway operators who have supported me since I
was running around railway structures holding a prototype
machine.
Being encouraged by this award, I will keep focusing on
the structures inspection technologies in order to ensure
the safety of railway operation and to save labor for
maintenance. I will be most grateful if I could ask for yourcontinued support.

The non-contact vibration measurement system
Left: U-Doppler II
Right: U-Doppler I

Annual Report 2020-2021

Railway Technical Research Institute

9. Numerical Simulation of Train Cabin Ventilation by OpenWindows
RTRI numerically simulated the ventilation of an urban commuter train with windows opened, using the
airflow simulator which RTRI developed, and evaluated its effect. (Fig.1)

Fig. 1 Cabin air ventilated through open windows[Outline of the result]・The volume of the air ventilated from a train window
is proportional to the opening areas and train runningspeeds.
・The volume of ventilated air is not substantially affectedby passenger occupancy rates.
・ When a train is running at 70 km/h with 6 windows
opened by 10 cm, the ventilated volume through thewindows is 0.36 m3/s, which means that the air inside
a train cabin is completely replaced with the fresh air
once every 5 to 6 minutes. The total ventilated volume
combining air conditioners and open windows is 0.78
m3/s, meaning that the cabin air is replaced once every
2 to 3 minutes.
* Ventiration rate = Ventilated air volume per second

Fig. 2

Ventilation rate by opening area

Annual Report 2020-2021[Evaluation of ventilation rate]* Posted on the website of the Ministry of Land,
Infrastructure, Transport and Tourism on June 5, 2020
Fig.2 and 3 shows some of the simulation results. Fig. 2
shows ventilated volumes from 6 windows with different
opening areas at train running speeds of 72 km/h and
45 km/h, with the passenger occupancy of 0% and Fig.3
shows the volumes at different speeds. As you can see
from these graphs, ventilated volumes are proportional
to opening areas and train speeds. For example, if a train
is running at 70 km/h with windows pulled down 10 cm(opening area 0.72 m2, the ventilated volume of the car
will be 0.36 m3/s. It means that the air inside the cabin
is completely replaced with the fresh air once every 5 to
6 minutes.

Fig. 3

Ventilation rate by train speed

53

News Release[Evaluation of the impact by occupancy]
Using the models assuming 50% and 100% passenger
occupancy shown in Fig.4, we analyzed the impact of
the occupancy on ventilation rate. In the 50% model,
all the seats are occupied and passengers are standing
near the doors and around the center of the aisles. In

54

the 100% model, passengers are standing with 10 to
20-centimeter distance from each other. The result ofventilation simulation under these conditions is shownin Fig.5, which indicates that the ventilated volumes
decrease only a little, even if the occupancy rates go up.

Fig. 4Passenger occupancy modelFig. 5Ventilation rate by occupancy

Annual Report 2020-2021

Railway Technical Research Institute[Evaluation of the effects by air-conditioners andfans]
Using a model assuming that air-conditioners and fans
are working (Fig.6), we have analyzed the relationshipbetween air-conditioning effects and ventilated volumes.
(Fig.7) The result is shown in Fig.8 and indicates that
air-conditioning has little impact on the air volumes

ventilated through open windows. Therefore, if a vehicle
is equipped with air-conditioners taking in outer air,the ventilation rate will be the total of the intake by airconditioners and the volume ventilated through windows.
For example, as is shown in Table 1, if the intake of outer
air by air-conditioners is 0.43 m3/s, the air inside a cabin
is completely replaced by fresh air every 2 to 3 minutes.

Fig. 6 Air-conditioned and fanned cabin modelFig. 7

Annual Report 2020-2021

Velocity distribution in a cabin55

News Release

Fig. 8

Table 1

Ventilation by windows and air-conditioning effects

Ventilation effects by air conditioners taking in outer air[Further confirmation and evaluation]
We will confirm the accuracy of the numerical simulation
through measurement on actual train vehicles and analyze
the ventilation effects of the sideway seat arrangement
and gangways. We will also evaluate in detail the airflow
at different areas of such vehicles.

56

Annual Report 2020-2021

Railway Technical Research Institute

10. Digital Maintenance Technical Seminar Held in Osaka
On October 22, at Hotel Mielparque Osaka, RTRI held Digital Maintenance Technical Seminar focusing
on labor-saving and remote operation in railway facilities maintenance.

RTRI has been addressing research into “labor savingby digital maintenance” in order to attain more laborefficient railway facilities maintenance by fully using
digital technologies. Currently, we are in the midst of
the Covid-19 pandemic and, in terms of minimizing theinfections, remote operation of facilities maintenance isincreasingly important.
This seminar was held with sufficient measures taken toprevent the coronavirus infections. 94 participants from 25
companies, mostly railway operators, joined the seminar.
Its keynote lectures were live-streamed and 64 viewers
from 36 companies watched them.
During the seminar, three keynote speeches in the fieldsof structures, track and earthquake engineering andeight presentations of our research results were made.
Two of the presentations were delivered in “hybrid” stylecombining live streaming from the test fields at RTRI
and presentations at the seminar venue, and six wereremotely done by researchers who explained from RTRI
about the panels, monitor screens, exhibited materials
and models at the seminar venue.

[Research presentations]Hybrid style
・ Visual inspection support system using 3-dimensional
images・Track maintenance database system LABOCS and its
future prospect
Remote style・Tunnel inspection support system using image data
・ Station renovation support system using passenger
flow simulation
・Track2er: low-cost track inspection device
・ Degradation assessment system for wood sleepers
using deep learning
・ Train inspection support system using image analysis
engine・DISER and supporting early reopening of train operation
after an earthquake using displacement sensor
For further details please contact:
https://www.rtri.or.jp/sales/inquiry.html[Keynote speeches]・Masayuki Koda
Director, Head of Structures Technology Division“Railway structures maintenance using digitaltechnologies: labor saving in construction work”
・ Hiroo Kataoka
Director, Head of Track Technology Division“Labor saving and safety improvement in track
maintenance using digital technologies”
・Shunroku Yamamoto
Director, Head of Center for Railway Earthquake
Engineering Research“Early reopening of train operation after an earthquake
using real-time data”

Annual Report 2020-2021

57

News Release

Keynote speech

Seminar venue

Live streaming of a keynote speech
Bottom-right is the speaker
Railway structures maintenance using digital technologies: labor saving in construction work

58

Annual Report 2020-2021

Railway Technical Research InstituteHybrid presentation

Presentation at the seminar venue
Visual inspection support system using 3-dimensional images

Live streaming from the test field reproducing railway structures at RTRI
Visual inspection support system using 3-dimensional images

Annual Report 2020-2021

59

News Release

Remote presentation

Participants asking questions at the seminar venue
Train inspection support system using image analysis engine

A researcher making remote presentation and answering questions from RTRI
Train inspection support system using image analysis engine

60

Annual Report 2020-2021

Railway Technical Research Institute

11. Digital Maintenance Technical Seminar Held in Tokyo
On November 11, at Yurakucho Asahi Square, Tokyo, RTRI held the Digital Maintenance TechnicalSeminar.

RTRI has been addressing the research into “labor savingby digital maintenance” in order to attain more laborefficient railway facilities maintenance by fully using digitaltechnologies. Currently, we are in the midst of the Covid19 pandemic and the need for labor-efficient, remotesystems for maintenance work is even more highlighted.
This seminar was thus focused on labor-saving andremote operation in railway facilities maintenance.
For holding this seminar, sufficient measures were taken
to prevent the coronavirus infections. 95 participants from35 companies, mostly railway operators, attended theseminar.
[Remote exhibition ]Following nine items of RTRI’s research results were
presented at this seminar, covering fields of structures,track, power supply and earthquake engineering. Panels
and monitors, real and model devices were exhibitedat each booth stands in the seminar venue, and theresearchers made presentations, answered questions
from the visitors and demonstrated the operation ofdevices remotely from RTRI in Kunitachi.

Annual Report 2020-2021[Presentations]
・ Visual inspection support system using 3-dimensional
images・ General inspection support system for tunnels using
image data・Track2er: low-cost track inspection device
・ Condition assessment system for wooden sleepers
using deep learning
・Condition assessment device for trackbed focusing on
the noise transmission property ofballasted track
・ Non-contact measurement device for overhead wireusing images
・ LABOCS, Database System for Railway Track
Maintenance, and its future prospect
・ Train inspection support system using image analysis
engine・Supporting early resumption of train operation after anearthquake using DISER, Damage Information System
for Earthquake on Railway, and displacement sensors

For further details please contact:
https://www.rtri.or.jp/sales/inquiry.html

61

News Release

Remote exhibition at the seminar venue

A researcher delivering a presentation and answering questions remotely from RTRI at Kunitachi

62

Annual Report 2020-2021

Railway Technical Research Institute

12. H
 igh-Speed Pantograph Testing Machine Completed
AT RTRI, a world’s highest level pantograph testing machine was completed (Fig. 1). This machine iscapable of moving a pantograph at high speeds up to 500 km/h, powered with large current from trolleywire and assessing the performance of Shinkansen’s pantographs. This machine will be used to develop
a pantograph which has an excellent performance to keep contact with trolley wire and is capable ofreducing contact loss. The mechanism of abrasion of pantograph contact strips and trolley wire will beanalyzed with this machine as well.

[Outline of the high-speed pantograph testingmachine]
The high-speed rolling disk is capable of:・ reproducing the moving state of a pantograph of ahigh-speed train running up to 500 km/h (Fig.1)
・ capable of reproducing contact-loss arcing between
a pantograph and trolley wire and increase in thetemperatures of parts by applying electrical current(maximum 600 V, 1000 A) to the pantograph and trolleywire, (Fig. 2)
・reproducing lateral and vertical displacement of trolleywire (Fig.3)

・conducting tests with real trolley wire (Fig.3)
・ reproducing vertical vibration of a train vehicle byshaking the pantograph stand vertically・conducting tests under the temperatures and humidities
controlled by air-conditioning device

Fig.1: High-speed pantograph testing Machine

Annual Report 2020-2021

63

News Release

Table 1: Specifications

Fig.2: Testing on the high-speed pantograph testing machine（Rotating speed: 360 km/h, AC200V, 600A）

64

Annual Report 2020-2021

Railway Technical Research InstituteFig.3: Rotating body and disk

[Further tests and development]
Evaluation of pantograph performance
We will evaluate the pantograph performance such as
contact-keeping performance and rate of contact loss.
Development of Shinkansen pantographs
We will efficiently develop pantographs for Shinkansensuch as active-controlled pantographs.

[Reference]
The performance of a conventional rolling disk machineused before is as follows:
Maximum rotating speed: 300 km/h
Power unit: 100V, maximum current 400A
No air-conditioning device

Development of sliding plates
We will improve the reliability of tests by properly
controlling testing conditions including temperaturesand humidities. Under the controlled conditions, we will
analyze the mechanism of abrasion of trolley wire andpantograph sliding plates and improve the efficiency inthe performance evaluation of sliding plates performanceand materials evaluation to extend their service live.
Analyizing causes of failure
By reproducing power supply conditions as well as verticaldisplacement of trolley wire, we will analyze failure causes
and develop preventive measures efficiently.

Annual Report 2020-2021

65

News Release

13. The 9th SNCF-RTRI Collaborative Research Seminar
held
The Railway Technical Research Institute (RTRI) and SNCF held their 9th Collaborative
Research Seminar on December 3, 2020.

RTRI and SNCF concluded an agreement on collaborative
research and related activities in 1995. Since then,both parties have been collaborating in many fieldsof railway technical research. They jointly organize a
research seminar every two years. There, they discuss
their management policies regarding collaborative
research, deliver presentations of the results of eachresearch project and set the plan and schedule of projects
for the next phase.
This 9th Seminar was attended by 40 people, including Mr.
Pierre Izard, Vice President, and Ms. Carole Desnost,
Director of Innovation and Research from SNCF, RTRI’s
President Watanabe and Executive Director Furukawa
and researchers of each project. The participants from
SNCF had planned to visit Japan and attend the seminar,
but due to the Covid-19 travel restrictions, the seminar
was held online.

(1) Management Meeting
The Management Meeting was attended by Vice President
Izard, President Watanabe and other executives. The
RTRI participants explained the outline of the master plan
started in 2020 “RESEARCH 2025” and their initiative
to introduce AI and other digital technologies to railway
operation. The SNCF participants explained the outlineof their research project “TECH4RAIL”※1 and “SNCF
initiative to realize carbon-free railways in 15 years” as
one of their energy-saving efforts. Both parties agreed tocontinue sharing information on the topics to use AI andother cutting-edge technologies.
※1 TECH4RAIL is a research and development planthat SNCF has been implementing since 2016, andZero Emission is set as one of its goals.

Management Meeting (Upper center: President Watanabe)

66

Annual Report 2020-2021

Railway Technical Research Institute(2) Presentation Meeting
Presentations were delivered on two collaborative
research projects and eight information exchange
projects of the 9th Phase from 2018 to 2020 andon two collaborative research projects and eight
information exchange projects of the 10th Phase from2020 to 2022, followed by lively discussions (Table 1).

on the amount and accuracy of machine learning data
used to predict delays in train operation, reflecting greatinterest of the participants in applying the state-of-the-art
technologies to railways.
The next collaborative research seminar will be held in
the fall of 2022 in Japan.

In particular, the presentation on the topic “Passengerdriven operation” was followed by active discussions
Table 1

9th Phase and 10th Phase Collaborative Research / Information Exchange Projects※2 Rail with bainite structure developed to suppress shelling, which is rail damage due to contact with wheels※3 Safety Integrity Level, defined in IEC61508. SIL4 is the highest level of safety (lowest probability of fatalities
due to equipment failure).

※4 Track maintenance machine that promotes the stability of the track by applying vibration to the track on therail after work such as track bed replacement.

Annual Report 2020-2021

67

News Release

Presentation Meeting (RTRI)

68

Annual Report 2020-2021

Railway Technical Research Institute

14. RTRI Develops a Risk Assessment System for Snowmelt
Disasters
RTRI has developed a risk assessment system for snowmelt disasters. (Fig. 1) This system assesses
the risk of slope collapses caused by snowmelt water in early spring, and the first one developed forrailways in Japan.

Fig. 1 The display of risk assessment system for snowmelt disasters

1. BackgroundIn early spring, snow areas are sometimes hit bysnowmelt disasters as well as avalanche. A large amountof snowmelt water as well as rainwater penetrates theground in spring and causes slope collapses. Although
the slope collapses by snowmelt occur less frequently
than landslides caused by rainfalls, they tend to cause
serious accidents such as derailments in addition to train
delays. Railway operators have intensified trackside
patrolling according to temperatures and, if necessary,lower the limit rainfall amount to restrict train operation
when snowmelt is progressing, but a method to determine
the necessity of patrolling based upon scientific standards
have been desired.

Annual Report 2020-2021

2. Characteristics of the system
・ The risk of snowmelt disasters is assessed every one
hour and the data is provided to railway operators todetermine the necessity of patrolling during the snowmelt
season.
・ As the data necessary to assess the risk is obtained
from AMEDAS (Automated Meteorological Data
Acquisition System) , building additional meteorologicalobservation facilities is not required.
・ By using two indicators, snowmelt amount and snow
depth, the risk of snowmelt disasters can be assessed
effectively.

69

News Release

3. Risk assessment with this system(1) Outline
The system consists of servers managed by RTRI andapplications with following functions.
・ Function to gather AMEDAS data on targeted areas
every one hour.
・Function to estimate snowmelt amount every one hour,
convert the data to effective snowmelt amount which isan index that has a strong correlation with ground waterlevels, and then assess the risk level by comparing theindex with the preset standard value.
・Function to post estimated effective snowmelt amounton the dedicated website and, if the values exceed thestandard value, indicate it on a monitor screen andinform users of the disaster risk level.

[Reference]
Estimation of effective snowmelt amount and otherindicesThis system uses effective snowmelt amount, as it isan index correlating strongly with ground water levels.
Effective snowmelt amount is calculated by subtracting
the outflow amount from inflow, that is, assuming that
effective snowmelt amount is retained water in a sort of atank with holes at its bottom. (Fig.3) By checking effective
snowmelt amount, it is possible to monitor changes in the
groundwater level over time and its peak season. (Fig.4)

Railway operators can check the assessment results forthe predetermined spots by accessing the website using
computers and tablets. The service is provided fee-based.
(2) The mechanism of snowmelt disasters and effectsof the risk assessment system
Snowmelt disasters take place following the 3 steps
shown in Fig. 2.
① Snowmelt water and rain penetrate the ground.
② The groundwater level rises.
③ The ground becomes unstable and slopes collapse.
This system estimates amounts of water penetrating the
ground using snow depth and effective snowmelt amountand assesses the impact on the step ③, slope stability.
Railway lines extend thousands of kilometers and therisk monitoring needs to cover wide areas. Consideringsuch conditions specific to railways, this systemassesses snowmelt disaster risks using four indices, airtemperature, precipitation, wind speed and sunlight hours
for every hour. The data is obtained from the nationwide
AMEDAS network data.
By introducing this system, railway operators will be able
to determine the necessity of patrolling based on scientificand quantitative indicators.
Fig. 2 Mechanism of snowmelt disasters

70

Annual Report 2020-2021

Railway Technical Research InstituteFig. 3 Calculation of effective snowmelt

Fig. 4 Changes in ground water level and effective snowmelt

Risk assessment
Since the probability of snowmelt disasters depends onsnow depth as well as effective snowmelt amount, eachyear’s maximum effective snowmelt amount for every
snow depth is calculated by the AMEDAS data for thetarget area for the winter in the past 20 years. Basedon the results, standard values of effective snowmelt
amount for each snow depth depending on reoccurrence
intervals (time periods between snowmelt disasters) are

Annual Report 2020-2021

predetermined (Fig. 5 (a)). The upper standard values
mean higher probability of snowmelt disasters. Then, inorder to check conditions of the target area, effective
snowmelt amounts for respective snow depth areplotted and connected with line (black line) based on
the AMEDAS data for every hour (Fig. 5 (b)). The risk isassessed by comparing the latest one of the plotted lines
with the predetermined standard values.

71News Release

Fig. 5 Risk assessment method

Setting standard values
If the standard values are set lower, railway operatorsneed to stay on alert longer, but If they are set higher,
disaster risks are less likely to be detected effectively.
Thus RTRI determines the standard values for this systemthrough coordination with railway operators.

72

Annual Report 2020-2021

YEAR 2020-2021: April 1, 2020 - March 31, 2021
Copyright© 2021 Railway Technical Research Institute.
All rights reserved.
Published by Atsushi FURUKAWA, Executive Director
International 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
Facebook: https://www.facebook.com/rtri.eng

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