Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 2273-2294 |
| Seitenumfang | 22 |
| Fachzeitschrift | KSCE journal of civil engineering |
| Jahrgang | 26 |
| Ausgabenummer | 5 |
| Frühes Online-Datum | 18 Feb. 2022 |
| Publikationsstatus | Veröffentlicht - Mai 2022 |
Abstract
To make structural seismic response simulation more efficient, a meta-model method which is based on the time delay neural network is proposed. And an accuracy evaluation method that considers the drift peak amplitudes and maximum amplitudes in each intensity as performance parameters is also proposed, this method can make a balance between accuracy and training time. Exampled by 4 frame structures which are all 20 stories, and accuracy evaluating results show that more than 80% of samples, which include training models and testing models of these performance parameters can be explained by meta models’ fitting. The average time to simulate by this method is 0.08s and faster than the finite element method which spends 24 min averagely.
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in: KSCE journal of civil engineering, Jahrgang 26, Nr. 5, 05.2022, S. 2273-2294.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Seismic Response Meta-model of High-Rise Fame Structure Based on Time-Delay Neural Network
AU - Zhang, He
AU - Bittner, Marius
AU - Beer, Michael
N1 - Funding Information: This research was funded by Junior Researcher Grant Yangzhou University (Grant No. 137012122), and this study was financially supported by the China Scholarship Council (CSC) (Grant No. 20180670149). Natural earthquake data were downloaded from PEER Strong Ground Motion Databases ( https://peer.berkeley.edu/peer-strong-ground-motion-databases ). And the calculation is supported by Institute for Risk and Reliability, University Hannover.
PY - 2022/5
Y1 - 2022/5
N2 - To make structural seismic response simulation more efficient, a meta-model method which is based on the time delay neural network is proposed. And an accuracy evaluation method that considers the drift peak amplitudes and maximum amplitudes in each intensity as performance parameters is also proposed, this method can make a balance between accuracy and training time. Exampled by 4 frame structures which are all 20 stories, and accuracy evaluating results show that more than 80% of samples, which include training models and testing models of these performance parameters can be explained by meta models’ fitting. The average time to simulate by this method is 0.08s and faster than the finite element method which spends 24 min averagely.
AB - To make structural seismic response simulation more efficient, a meta-model method which is based on the time delay neural network is proposed. And an accuracy evaluation method that considers the drift peak amplitudes and maximum amplitudes in each intensity as performance parameters is also proposed, this method can make a balance between accuracy and training time. Exampled by 4 frame structures which are all 20 stories, and accuracy evaluating results show that more than 80% of samples, which include training models and testing models of these performance parameters can be explained by meta models’ fitting. The average time to simulate by this method is 0.08s and faster than the finite element method which spends 24 min averagely.
KW - Frame structure
KW - Metamodel
KW - Neural network
KW - Seismic
KW - SRC-RC frame
UR - http://www.scopus.com/inward/record.url?scp=85124759645&partnerID=8YFLogxK
U2 - 10.1007/s12205-022-0878-7
DO - 10.1007/s12205-022-0878-7
M3 - Article
AN - SCOPUS:85124759645
VL - 26
SP - 2273
EP - 2294
JO - KSCE journal of civil engineering
JF - KSCE journal of civil engineering
SN - 1226-7988
IS - 5
ER -