一般財団法人 大成学術財団
2018 年度助成研究梗概書
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
横浜国立大学 ○1*シリンゴリンゴ
ディオニシウス筑波大学
2**西尾真由子，
横浜国立大学
3**藤野陽三橋梁
免震1.

地震応答モニタリング

マンリ―

損傷検知

ウェーブレット変換

Introduction(movable) bearing. The girder response is characterized by low

This study describes implementation of techniques for detecting

frequency component indicating the effect of softening by isolator.

bearing malfunction in a multi-span continuous bridge using time-For a functioning (movable) bearing there is a frequency shift tovarying identifications of the seismic records. The techniques

higher frequency of the pier responses compared to the girder

include wavelet analysis, sparse representation, and machine

response as shown in Fig.2.b. Meanwhile frequency characteristics

learning techniques for real-time detecting bearing malfunction in aof the pier supporting the malfunction (locked) bearing is dominatedmulti-span continuous girder bridge. The method is implemented toby the frequency like that of the girder Fig.2.c.. This suggests that at

the seismic records of a multi-span continuous girder isolated bridge

the locked bearing the pier and girder move in the similar manner.

obtained from long-term monitoring system of Shin-Nakagawa andUsing DWT, the time of yielding process of bilinear system is traced

Katsuta Bridge.

from the associated signal discontinuity as shown in Fig. 3. Thus,
we can classify the unlocked and locked piers based on the details

2.

Methodology

component (D1) obtained from discrete wavelet transformation.

In this study Continuous Wavelet transform (CWT) is utilized todetect shifts in the vibration frequency of the recorded seismic3.

accelerations of the structure by extracting the instantaneous

The methodology was applied to Katsuta viaduct, a continuous

Implementation on Katsuta Bridge

frequency from wavelet ridges. This is based on the premise thatmulti-span girder bridge that was monitored by wireless sensor

seismic isolation behavior in normal or expected condition and in

network using 20 accelerometers. The viaduct is located next toabnormal or unexpected condition under earthquake excitation

Shin-Nakagawa Cable Stayed Bridge and about 30 earthquake

would produce a distinct pattern of structural stiffness and change

events were successfully recorded between 2018-2020 [2] (Fig.4).

of the fundamental frequency. Discrete Wavelet Transform (DWT)
is utilized to detect the irregularities in the high frequency responseof the structure by investigating the detail functions (Di). Sparse
Computation and wavelet are implemented to decompose the signal
in discrete wavelet transform. Machine Learning technique isemployed for Classification and Regression of the Detailcomponent (Di). Details on the methodology is described in Fig. 1.
Numerical simulation was conducted to investigate and verify theproposed method using multi-degree-of-freedom bridge model.
The isolation bearings were model with bilinear hysteretic modeand bearing malfunction (locking of the transverse side stopper) is
modeled as the sudden increase in the initial stiffness of the bearing.
From wavelet time-frequency map and Instantaneous Frequency

Fig.1. Methodology for Detection of bearing malfunction using
CWT & DWT Analysis [1](Fig 2) we can identify conditions where the pier cap having

Two largest earthquakes March 30, 2018 and July 17, 2018 are usedmalfunction bearing (locked) and the pier cap having functioning

as examples of application. From accelerations of the girder andReal-Time Structural Damage Evaluation of Bridges from Seismic
Monitoring Records Using Machine Learning and Sparse
Representation

Author’s Name１：Dionysius Manly SIRINGORINGO
Author’s Name 2：Mayuko Nishio
Author’s Name 3：Yozo Fujino

piers, the wavelet map was derived using procedure described inchange in the detail components of DWT decomposition of theFig.1. Fig. 5 and 6 show examples of result of analysis from Marchacceleration signal.

30, 2018 earthquake and July 17, 2018 earthquake, respectively.
The from wavelet time-frequency map of acceleration responses
from the girder and pier cap were analyzed to identify the bearing
condition during the earthquakes.

Fig.2.CWT time-frequency map and Instantaneous Frequency(IF) of acceleration responses from (a) continuous girder, (b) pierwith movable bearing (c) pier with locked isolator bearing.

Fig. 4. Shin-Nakagawa (top left) & Katsuta Bridge(top right),
Wireless monitoring system of Shin-Nakagawa and Katsuta
Viaduct (bottom).

Fig. 5. CWT& IF results March 30, 2018 EQ. (a) girder (lowfrequency dominant), (b),(c) Piers (high frequency shift (4~6 Hz).

Fig.3. Example of simulation using discrete wavelet transform todetect the signal discontinuity associated with yielding ofbilinear isolator system.
The objective is to determine whether the frequency shift of the pierresponse occur or not. Occurrence of frequency shift indicate that

Fig. 6. CWT& IF results July 17, 2018 EQ. (a) girder (lowfrequency dominant), (b),(c) Piers (high frequency shift (4~6 Hz).

the pier cap has functioning (movable) bearing. The figure clearlyshows that for both earthquakes, the isolator bearing on pier 38 and4.

41 have functioned properly (movable) [1]. The results demonstrate

In this research information from both continuous wavelet anddiscrete wavelet transform with learning process were used toevaluate bearing condition directly from the recorded response,
once earthquake occurs and signal is received without performing
detailed structural analysis of the bridge.
References:[1]. Siringoringo DM, Fujino Y. Application of Wavelet-Basedthat in a moveable bearing, the pier and girder response during thetime interval of peak excitation were dominated by their own
respective frequency suggesting they are uncoupled system. In thiscondition, the piers are isolated from the girder and the highfrequency content of the pier is not transferred to the girder, whichis expected from a functioning isolator bearing.

Conclusion

Additionally, abrupt change in D1 were associated with the time

Analysis for Detection of Isolation Bearing Malfunction in Multi-locations at the onset of both yielding and unloading. This

span Continuous Girder Bridge (in preparation for submission to

information can be utilized to indicate condition of the isolator

SCHM 2020)

bearing. The premise is that at the time when a functioning[2]. Siringoringo DM, Fujino Y, Mehta V, Suzuki M, Continuous

elastomeric isolator bearing enters nonlinear stage during large

Seismic Monitoring of Seismically Isolated Bridge by Wirelessexcitation, abrupt change in the details D1 could be detected. OnSensor Network, 17WCEE, Sendai, Japan 2021.

contrary, the yielding mechanism will not be initiated when thebearing is locked, and as a result there would not be any abrupt*所属１ 横浜国立大学 先端科学高等研究院**所属 2 筑波大学 システム情報系構造エネルギー工学域**所属 3 横浜国立大学 先端科学高等研究院*Organization 1, 3 Yokohama National University, Institute ofAdvanced Sciences**Organization 2 University of Tsukuba