The behavior analysis of

Spatial Singular Mode Angle
due to addition of noise to the data
in an actual bridge experiment
the 10th EWSHM in Parelmo
2022, July 4.
Yuta Takahashi
Yachiyo engineering, Japan.

Naoki Kaneko, Ryota Shin, Kyosuke Yamamoto(Presenter)
University of Tsukuba, Japan.

1Our Theme
Practical and Robust Drive-by Monitoring• Cyber Physics System (CPS) realizes the data-driven management.
• The construction needs BIG data for infrastructure, bridge, road surface etc…
• This study focuses on the bridge.

• The measurement should be practical.
• The COST is required as being low (about energy supply, data communication, the sensor installation)• Drive-by monitoring can reduce the cost of the sensor installation.

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Data driven SHM for bridge• The measurement by sensors on bridge may be labor-consuming.
• The sensors on vehicle can collect a lot of data from many bridges only by running.
• Their popular damage indices are natural frequency (NF) and mode shape (MS).
• NF are often affected by noise, and it is necessary to use expensive sensor for detection of damage.
• MS is more sensitive, however, it requires precise allocation.

Bridge Monitoring

Drive-by Monitoring

ROADCLOSED3

Application of Drive-by Monitoring• Collect big data by vehicles with vibration sensors and GPS• Extract bridge vibration components from the vehicle vibration data
• Evaluate the bridge condition based on the estimated bridge vibration• Inspect only “damage-suspected” bridges
Detailed Inspection

Bridge Screening

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Drive-by Technology for Screening: SSMA
• Spatial Singular Mode Angle is Estimated Bridge Mode ShapeMeasure the un-sprung
vehicle vibrations

Estimate the bridge vibration
First Mode×𝐍𝑆𝑆𝑀𝐴tanSVD

Interpolation:(Conversion from the travelling points to the point fixed on bridge)

Second Mode𝐴𝐴𝒙 𝒚 𝑡= 𝐔 ×𝚺 × 𝑽tan𝐴𝐴5

Previous Study• SSMA is relatively robust and sensitive.
(not affected by noise)(changes well for structural changes)

Runningrale

Numerical Model
DamageDamagedgirderFirst ModePin
Second Mode○:Intact○:Damage

Angle of mode shape2827262524
230.95

500.9

45

0.85
0.8
0.75

222120SSMA [rad]291020
Vehicle run speed [km/h]

400.79●:Intact●:Damage
9.51010.5Run speed [rpm]

11SSMA[degree]

30

IntactDamaged

40353025204681012Vehicle Speed[m/s]146Tendency of SSMA and Bridge Span
• SSMA tends to depend on bridge length
90 +90ASHAHI2

ASHAHI2+ MATSUMI1+ MATSUMI1

SSMA [deg.]+8080

SHIRAHATA2
TAKAMARU2706060+ ++50010+2030
40Speed [km/h]

SHIRAHATA2
TAKAMARU270+++++++5060

501216202428Bridge Span Length [m]32* Y.Takahashi et al, EVACES2021

7The Purpose of this Study• To try to decrease the variance of SSMA by two schemes
1. Noise Adding
2. Smoothing

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Method: Noise and Smoothing• Noise Adding• Original signals are amplified by Noise (ratio: ±5~10%)• Notice that the additional noise is white noise.

• Smoothing• The smoothed signal are produced by Gaussian Filter from the original. (Low-Pass Filter)• Window size is changed to 4 (~75Hz), 20 (~15Hz), and 40 (~7.5Hz).
Original

Front
Rear

Add 10% Noise
Front
Rear

Smoothed
Front
Rear

9Experiment• The field experiment is carried out for 4 bridges
Bridge (name)

TypeSpan [m]Run [times]

PC1(TAKAMRU)

PC

12.6

26

PC2(SHIRAHATA)

PC

1426

PC3(MATSUMI)

PC

30.88

25

S1(ASAHI)

Steel

30

24

PC1

PC2

13.8t

S1

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Experiment Movie : PC3

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Example of Measured Data
Electrical noise ?

Mid-spanof the Bridge

Unsprungof the Vehicle

Peak

Rear

Natural Frequenciesof the vehicle?

Rear

Front
Front

Acceleration

FFT12

Result• Noise Adding: a little difference of SSMA from original.
• 10% random noise is high on previous studies**.
• Only S1 tends to be converged in spite of increasing noise ratio.

** For example,Eugene J Obrien, 2017.

• Smoothing: the variance decreases better in longer PC bridge.
• Bridge length: PC1<PC2<(S1)<PC3.
• Over 20 window size, the variance decrease is not clear, comparing with window size 4 on PC3.
• Smoothing increases the variance of S1.

10% 5% 4

AN20LPF
PC1

4010% 5% 420ANLPF
PC240AN
10% 5%4LPF
20PC3AN40

10% 5% 4LPF
2040S113

Discussion
Variance change of SSMA

Noise-Adding

Window SizeBridge

10%5%042040PC10.231

0.214

0.200

0.194

0.197

0.180PC2

1.764

1.752

1.739

1.648

1.562

1.273

PC3

1.627

1.467

1.321

0.485

0.362

0.437

S1

2.043

2.058

2.076

2.100

2.217

2.249

• In PC Bridges, Smoothing can decrease the SSMA variance:• PC bridge is more “rigid”,• In a Steel Bridge, Noise-Adding can decrease the SSMA variance:• Noise can disturb the influence from unknown factors, while smoothing deletes the structural information
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Conclusion & Future Works• Conclusion• Noise-Adding and Smoothing can decrease the SSMA variances• On PC bridges, Smoothing can work well, while Noise-Adding doesn’t.
• On the steel bridge, Noise-Adding can work well, while Smoothing doesn’t.

• The difference of bridge type should be considered for variance-reduction.

• Future Works• Field Exp. on 121 bridges has been done:• We will analyze SSMA distributions from Length, Type and Damage.

Innovative solutions for the society

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Acknowledge & Reference
Acknowledge:
This study use sensors developed by Mr. Ono who is engineer stuff in university of Tsukuba.
We appreciate his great works.

IN SLIDES:* Yuta Takahashi et al, The validation of sensor on-vehicle for evaluation of actual bridges with signal processing,9th International
Conference on Experimental Vibration Analysis for Civil Engineering Structures (EVACES2021)**Eugene J Obrien et al, Application of empirical mode decomposition to drive-by bridge damage detection, European Journal of
Mechanics-A/Solids, 2017.
•Y B Yang et al, Extracting bridge frequencies from the dynamic response of a passing vehicle, Journal of Sound and Vibration,
2004.

• T. Nagayama et al, Bridge natural frequency estimation by extracting the common vibration component from the responses oftwo vehicles. Engineering Structures, 2017.
• K. Yamamoto and M. Ishikawa, Numerical Verification of Bridge Screening Technology based on Vehicle Vibration. Lecture Notesin Engineering and Computer Science: Proceedings of The World Con-gress on Engineering 2016, 2016.

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