Research Background• Performance of isolated bridge depends on performance of isolators.
Yokohama National University

Real-Time Structural Damage Evaluation of Bridges
from Seismic Monitoring Records Using Machine
Learning and Sparse Representation
機械学習とスパース推定法を利⽤した地震応答
モニタリング記録からの橋梁のリアルタイム損傷検知• In the past earthquakes, there were cases where isolators failed tofunction properly because side stopper were locked. As a result, deck was
not isolated completely, and unexpected load redistribution occurred.
• Monitoring system can be used to evaluate isolation bearing condition andprevent the failure.
免震⽀承，ゴム⽀承では⽀承が地震時に期待通りの挙動（性能）を発揮するかがキー．
事実，⽀承が地震で破損する例が⾮常に多い．
下の写真にあるように，サイドブロックが⽀承の橋軸⽅向の動きを拘束するケースが間々ある．
地震応答モニタリングが⽀承が地震時に想定通り動いているかを判断できるか？
side stopper unlockedwith isolator bearing

side stopper lockedwith isolator bearing

Dionysius M. Siringoringo, Ph.D

上フランジの食い込み

D. M. シリンゴリンゴ
Institute of Advanced Sciences (IAS)
Yokohama National University
横浜国立大学 先端科学高等研究院

Research Objectives• Develop a structural monitoring system to detect isolation bearing
malfunction directly from seismic records using machine learningand sparse representation.
• Implement the detection system to full‐scale monitoring of multi‐
span continuous girder isolated bridge using wireless sensor
network.
• 地震応答記録から⽀承の挙動を判断できるシステムを実記録から，応答シミュレーションから機械学習を使って構築する．

Methodologies

応答記録のウェーブレット変換を基本に• Continuous Wavelet Transform (CWT) : to detect shifts in the vibration frequency ofaccelerations by extracting the instantaneous frequency (IF) from the continuous
wavelet time‐frequency map (Grossmann and Morlet 1984).
𝑊 𝑎, 𝑏 ≅12𝑎

Λ 𝑡 Λ𝑡𝑏𝑎𝑒𝑑𝑡• Discrete wavelet transform (DWT) (4th Order Daubechies wavelet) : to detect
irregularities in the high frequency of acceleration. By sparse representation analysis
the irregularities are associated with the change in the detail component (Di) causedby stiffness characteristics of isolation bearing.
𝑥 𝑡𝐴𝐷• K‐means Clustering Algorithm (Machine Learning) (Arthur & Vassilvitskii 2007): toperform partition of data space (CWT & DWT results) into clusters associated withnormal and malfunction bearing via data training and pattern recognition. Euclidean
metric is computed to determine the distance between the clusters:𝐷𝑖𝑠𝑡 𝑈, 𝑉𝑈𝑉𝑢𝑣

研究の流れFlowchart of Analysis
Seismic records fromWireless Sensor
NetworkFEMによる数値解析モデルの構築 5径間連続，ゴム⽀承• Condition of locked (malfunction) bearing is simulated numerically by Finite
Element model using nonlinear structural dynamics.
• Locked bearing is model by changing the hysteresis model of isolator withadded friction.

Data Preprocessing(Selection, cleansing, &removal of traffic effect)

Continuous Wavelet Transform Analysis

Discrete Wavelet Transform Analysis

Determination of Morlet
wavelet parameters

Sparse Representation Analysis &Discrete wavelet Decomposition

Response Evaluation based on
Wavelet Time-Frequency Map

Finite Element Simulation

Machine Learning-based KCluster Classification of Details
wavelet Components (Di)

Machine Learning-based K-Cluster
Classification based on Instantaneous
Frequency (IF) criteria

Evaluation of Isolation Bearing Condition• 3D‐Continuous girder isolated bridge with 5 piers.
• Model made in SAP 2000.
• Input : Various‐scale 1995 Kobe Earthquake Takatori.

Structural Element
GirderPierBent
Bearing Properties
Initial stiffness (kN/m)
Post Yield Stiffness ratio
Yield Strength (kN)
Ultimate Disp. (m)Stiffness of Side Stopper
Initial stiffness (ton/m)
Post Yield Stiffness ratio
Yield Force (ton)CrossSection
Area(m2)
5.80
3.00
1.44
Mid Pier
3000
0.2
312.5
0.15
30000
0.2
15000

Cross Section
Moment of Inertia[Ixx, Iyy, Iq] (m4)
[14.48, 2.26, 5.9]
[0.57,1.00, 1.215][0.18,0.18,0.292]
End Pier
5000
0.2
312.5

Simulation of Hysteretic Isolation Bearing condition in thecase of Locked Bearing

Results of Finite Element Simulation

Results of Finite Element Simulation

Results of simulation show that bearing malfunction can be detected from
1. Pattern of Instantaneous Frequency Shift from Continuous Wavelet Transform.

Results of simulation show that bearing malfunction can be detected from
1. Pattern of shift in Instantaneous Frequency from CWT.
2. Pattern of Large spikes on the High‐frequency detail function from DWT.

桁の応答

橋脚の応答（正常）Pier with normal bearing
Girder

正常：免震効果が発揮

橋脚の応答（桁の応答に追随）

Pier with locked bearing

Frequency shift

⼀時，⽀承がロック⽀承が完全にロック状態

ウェーブレット変換からの⽀承の挙動の把握

K‐means Clustering of FE Simulation ‐ CWT Results

Finite Element Simulations for Locked Bearings
ロックされた⽀承の挙動把握

FEM応答シミュレーションより• The distance between two clusters of instantaneous frequencies (𝑈 , 𝑈 ) is normalizedwith the absolute difference between the pier freq (𝜔 ) and girder frequency 𝜔 ineach case, called the normalized distance (ND):
𝑁𝐷 𝑈 , 𝑈𝑈𝜔𝑈𝜔ND  1 (normal bearing)
ND  0 (locked bearing)

Result of Bearing Classification based on CWT & K−means Clustering

32ケース

1. All bearings function normally. (No)すべて正常

2. One pier with locked bearings (P1,P2,..P5) = 5 cases.

⼀つの橋脚がロック

3. Two piers with locked bearings (P12, P13,..P45) = 10 cases. 2つの橋脚がロック
4. Three piers with locked bearings(P123, P124,… P345) =10 cases 3つの橋脚がロック
5. Four piers with locked bearings (P1234,…,P1345) =5 cases 4つの橋脚がロック
6. All bearings at five piers locked (All)すべての橋脚がロック
For each case:
1. Nonlinear seismic response calculated.
2. CWT & DWT Analysis conducted.
3. K‐means Clustering analysis is performed

上のND指標でロックかアンロックかの判定がX-axis: Simulated Cases (Pier with locked bearing)X-axis: Simulated Cases (Pier出来ていることを確認できたwith locked bearing)K‐means Clustering of FE Simulation ‐ DWT Results• The distance between the cluster of normalized D1 to the threshold value is computed forthe interval time of peak excitation.
• The normalized distance (ND) between the cluster’s centroid of detail components DWT(Uc) and the threshold value (𝑈 ) are used to define condition of isolation bearing.
𝑁𝐷 𝑈 , 𝑈𝑈𝑈𝑈Y-axis: ND values from classification

Damage scenarios (32 cases):

ND > 0 (normal bearing)
ND < 0 (locked bearing)

Implementation on Katsuta Bridge
勝田高架橋

勝田高架橋 (平成13年完成)
・茨城県ひたちなか市に位置する
12径間連続非合成鋼鈑桁橋
・橋長L=383m
・すべりゴム支承（P35，A2）
積層ゴム支承 （P36〜P46）を使用

Wireless sensor node（Google マップより）

Y-axis: ND values from classification

Result of Bearing Classification based on DWT & K−means Clustering

橋脚の無線センサー

上のND指標でロックかアンロックかの判定がX-axis: Simulated Cases (Pier出来ていることを確認できたwith locked bearing)X-axis: Simulated Cases (Pier with locked bearing)

桁の無線センサー

Earthquake List Recorded on Katsuta Bridge (2018-2020)• List of earthquakes recorded on Katsuta Bridge between 2018‐2020.
• The seismic responses were recorded from Wireless Sensor Network (WSN)
EQ

DateEpicenter

Depth (km)Magnitude(M)Distance(km)PGA(cm/s2)

EQ1

7/17/201836.43N,140.7E

52

M4.8

15143.7

EQ2

6/17/201936.50N,140.6E

80

M5.1

16137.4

EQ3

3/30/201836.44N,140.6E

56

M5.1

11121.8

EQ4

6/4/2020

36.4N,140.7E

50

M4.7

14116.6

EQ5

1/21/2020

36.4N,140.7E

50

M4.3

14

61.38

EQ6

4/12/2020

36.2N,140.0E

50

M5.1

53

42.48

EQ7

6/1/2020

36.2N,140.4E

100

M5.3

2231.64EQ8

1/14/2020

36.1N,139.9E

50

M5.06626.78

EQ9

9/5/2018

36.4N,141.3E

60

M5.5

72

25.02

EQ102/1/2020

36.0N,140.1E

70

M5.3

57

23.57

EQ11

8/4/2019

37.7N,141.7E

50

M6.4

181

16.03

EQ12

5/17/201836.35N,140.6E

52

M5.3

75

10.4

EQ13

6/18/2019

38.6N,139.5E

10

M6.8

266

9.51

EQ14

7/7/2018

35.3N,140.6E

70

M6.0

118

7.98

EQ15

4/18/2020

27.2N,140.7E

50

M6.91018

2.48

Assessment of Bearing Condition by K‐means Clustering
based on CWT Results

ND  1 (normal bearing)
ND  0 (locked bearing)

CWT, Largest Earthquake (EQ1, July 17, 2018)

GirderPier

IF of CWT

SmallestEarthquake

実応答データにおいてもND指標でロックかアンロックかの判定が出来ていることを確認できた

Smallest Earthquake (EQ15, April 18,2020)

GirderIF of CWTFrequency
shift

DWT, Largest Earthquake (EQ1, July 17, 2018)

Large earthquake  Large spike on detailcomponents of DWT crosses the threshold value

15の地震からの地震記録

LargestEarthquake

Characteristics of Instantaneous Frequency of CWT and Details
Components of DWT from Seismic Records on Katsuta Bridge

Pier
IF of CWTNo Frequency
shift

DWT, Smallest Earthquake (EQ15, April 18,2020)

Small earthquake  Detail components of DWT
are below the threshold value

Assessment of Bearing Condition by K‐means Clustering
based on DWT Results

LargestEarthquake

ND > 0 (normal bearing)
ND < 0 (locked bearing)

Smallest
Earthquake

実応答データにおいてもND指標でロックかアンロックかの判定が出来ていることを確認できた

Conclusions•

Continuous and Discrete Wavelet with Sparse representation techniques wereemployed to detect malfunction of isolation bearing directly from seismic response.
Classification of bearing condition is made by machine-learning based K-cluster
technique.

•The techniques were verified in the finite element simulation and implemented on aKatsuta Bridge, a continuous multi-span girder bridge using seismic responses from
15 earthquakes recorded by wireless sensor network.

•Results show that isolation bearings have functioned properly without observed
locked bearing during large (PGA>100cm/s2) and moderate (10 cm/s2 < PGA < 100cm/s2) earthquakes. Possible locked bearing was observed during small
earthquakes (PGA<10cm/s2) because the isolation mechanism has not been
initiated due to the small amplitude of excitation.

ゴム⽀承の⾼架橋を対象に，⽀承が地震時にロック（機能不全）かアンロック（機能）かを橋脚の地震応答のウェーブレット解析
からの正規化指標から判定できることを，シミュレーションならびに，ワイヤレスセンサーで2年ほど地震計測している勝⽥⾼架橋（茨城県）の実応答データから明らかにした．