抄録
In this paper we propose the use of artificial neural network (ANN) emulators for an acceleration-based approach to evaluating building structures under earthquake excitation. The input layer of the ANN is a forced vibration, described as ground acceleration and the acceleration data of several floors. The approach is improved by using the acceleration at later time steps as the output of the neural network (NN). This time delay is optimized as a tunable band to provide the most sensitive signals. Minimally, this approach requires only one sensor, making it highly practicable and flexible. It is applicable to structures under diverse excitations including even very small impacts. Based on numerical simulation of a 5-story shear structure, we determined appropriate parameters for use of an NN and studied the generality and efficacy of the approach. The damage index, the relative root mean square error, was calculated for the case of a single structural damage as well as for cases of double damages at different damage locations, and appropriate parameters for the NN emulator were proposed according to the damage patterns. Variant ground motions were used to certify the generality of the approach. The numerical simulations of the proposed approach were verified experimentally.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 901-920 |
| ページ数 | 20 |
| ジャーナル | Structural Control and Health Monitoring |
| 巻 | 15 |
| 発行部数 | 6 |
| DOI | |
| 出版物ステータス | Published - 2008 10 |
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ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Mechanics of Materials
これを引用
Acceleration-based damage indicators for building structures using neural network emulators. / Qian, Yuyin; Mita, Akira.
：: Structural Control and Health Monitoring, 巻 15, 番号 6, 10.2008, p. 901-920.研究成果: Article
}
TY - JOUR
T1 - Acceleration-based damage indicators for building structures using neural network emulators
AU - Qian, Yuyin
AU - Mita, Akira
PY - 2008/10
Y1 - 2008/10
N2 - In this paper we propose the use of artificial neural network (ANN) emulators for an acceleration-based approach to evaluating building structures under earthquake excitation. The input layer of the ANN is a forced vibration, described as ground acceleration and the acceleration data of several floors. The approach is improved by using the acceleration at later time steps as the output of the neural network (NN). This time delay is optimized as a tunable band to provide the most sensitive signals. Minimally, this approach requires only one sensor, making it highly practicable and flexible. It is applicable to structures under diverse excitations including even very small impacts. Based on numerical simulation of a 5-story shear structure, we determined appropriate parameters for use of an NN and studied the generality and efficacy of the approach. The damage index, the relative root mean square error, was calculated for the case of a single structural damage as well as for cases of double damages at different damage locations, and appropriate parameters for the NN emulator were proposed according to the damage patterns. Variant ground motions were used to certify the generality of the approach. The numerical simulations of the proposed approach were verified experimentally.
AB - In this paper we propose the use of artificial neural network (ANN) emulators for an acceleration-based approach to evaluating building structures under earthquake excitation. The input layer of the ANN is a forced vibration, described as ground acceleration and the acceleration data of several floors. The approach is improved by using the acceleration at later time steps as the output of the neural network (NN). This time delay is optimized as a tunable band to provide the most sensitive signals. Minimally, this approach requires only one sensor, making it highly practicable and flexible. It is applicable to structures under diverse excitations including even very small impacts. Based on numerical simulation of a 5-story shear structure, we determined appropriate parameters for use of an NN and studied the generality and efficacy of the approach. The damage index, the relative root mean square error, was calculated for the case of a single structural damage as well as for cases of double damages at different damage locations, and appropriate parameters for the NN emulator were proposed according to the damage patterns. Variant ground motions were used to certify the generality of the approach. The numerical simulations of the proposed approach were verified experimentally.
KW - Building structures
KW - Damage identification
KW - Damage indicators
KW - Neural network emulator
KW - Structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=55349117117&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=55349117117&partnerID=8YFLogxK
U2 - 10.1002/stc.226
DO - 10.1002/stc.226
M3 - Article
AN - SCOPUS:55349117117
VL - 15
SP - 901
EP - 920
JO - Structural Control and Health Monitoring
JF - Structural Control and Health Monitoring
SN - 1545-2255
IS - 6
ER -