Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 109750 |
| Fachzeitschrift | Computer physics communications |
| Jahrgang | 315 |
| Frühes Online-Datum | 5 Juli 2025 |
| Publikationsstatus | Veröffentlicht - Okt. 2025 |
Abstract
Seismic metastructure based on phononic crystal theory provides a possible solution to accurately manipulating surface acoustic waves. However, the prediction of gradient seismic metastructure for transmission spectra in clay remains a significant challenge due to the damping characteristics of actual soils and practical engineering factors, which become a research hotspot in recent years. Based on finite element analyses and machine learning techniques, this work proposed a data-driven method for building a general prediction model of embedded pillar seismic metastructure with different multi-resonator gradients in the clayed soil. We employed a multilayer perceptron (MLP) model, with the multi-resonator gradients of the metastructure as the input, to predict the transmission spectra. To achieve input standardization, we applied an Autoencoder (AE) to construct a unified representation of the inputs. Due to the inherent non-linearity and variability in soil-structure interactions, the attenuation zones prediction results can only offer approximate engineering applications under specific conditions. By utilizing machine learning, our method achieves better generalization and can be adapted to a wider range of metastructure configurations. This research not only advances the gradient seismic metastructure design framework but also opens new avenues for practical applications in surface acoustic wave management.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Hardware und Architektur
- Physik und Astronomie (insg.)
- Allgemeine Physik und Astronomie
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in: Computer physics communications, Jahrgang 315, 109750, 10.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Machine learning for transmission spectra prediction on gradient seismic metastructure
AU - Rao, Yilin
AU - He, Liangshu
AU - Jin, Yabin
AU - Zhu, Hehua
AU - Rabczuk, Timon
AU - Zhuang, Xiaoying
N1 - Publisher Copyright: © 2025 Elsevier B.V.
PY - 2025/10
Y1 - 2025/10
N2 - Seismic metastructure based on phononic crystal theory provides a possible solution to accurately manipulating surface acoustic waves. However, the prediction of gradient seismic metastructure for transmission spectra in clay remains a significant challenge due to the damping characteristics of actual soils and practical engineering factors, which become a research hotspot in recent years. Based on finite element analyses and machine learning techniques, this work proposed a data-driven method for building a general prediction model of embedded pillar seismic metastructure with different multi-resonator gradients in the clayed soil. We employed a multilayer perceptron (MLP) model, with the multi-resonator gradients of the metastructure as the input, to predict the transmission spectra. To achieve input standardization, we applied an Autoencoder (AE) to construct a unified representation of the inputs. Due to the inherent non-linearity and variability in soil-structure interactions, the attenuation zones prediction results can only offer approximate engineering applications under specific conditions. By utilizing machine learning, our method achieves better generalization and can be adapted to a wider range of metastructure configurations. This research not only advances the gradient seismic metastructure design framework but also opens new avenues for practical applications in surface acoustic wave management.
AB - Seismic metastructure based on phononic crystal theory provides a possible solution to accurately manipulating surface acoustic waves. However, the prediction of gradient seismic metastructure for transmission spectra in clay remains a significant challenge due to the damping characteristics of actual soils and practical engineering factors, which become a research hotspot in recent years. Based on finite element analyses and machine learning techniques, this work proposed a data-driven method for building a general prediction model of embedded pillar seismic metastructure with different multi-resonator gradients in the clayed soil. We employed a multilayer perceptron (MLP) model, with the multi-resonator gradients of the metastructure as the input, to predict the transmission spectra. To achieve input standardization, we applied an Autoencoder (AE) to construct a unified representation of the inputs. Due to the inherent non-linearity and variability in soil-structure interactions, the attenuation zones prediction results can only offer approximate engineering applications under specific conditions. By utilizing machine learning, our method achieves better generalization and can be adapted to a wider range of metastructure configurations. This research not only advances the gradient seismic metastructure design framework but also opens new avenues for practical applications in surface acoustic wave management.
KW - Gradient metamaterial
KW - Machine learning
KW - Non-hermitian system
KW - Seismic metastructure
KW - Surface acoustic wave
UR - http://www.scopus.com/inward/record.url?scp=105010204918&partnerID=8YFLogxK
U2 - 10.1016/j.cpc.2025.109750
DO - 10.1016/j.cpc.2025.109750
M3 - Article
AN - SCOPUS:105010204918
VL - 315
JO - Computer physics communications
JF - Computer physics communications
SN - 0010-4655
M1 - 109750
ER -