Machine learning for transmission spectra prediction on gradient seismic metastructure
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.
Details
- Organisationseinheit(en)
-
Institut für Photonik
- Externe Organisation(en)
-
Tongji University
Fudan University
Bauhaus-Universität Weimar
- Typ
- Artikel
- Journal
- Computer physics communications
- Band
- 315
- ISSN
- 0010-4655
- Publikationsdatum
- 10.2025
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Hardware und Architektur, Allgemeine Physik und Astronomie
- Elektronische Version(en)
-
https://doi.org/10.1016/j.cpc.2025.109750 (Zugang:
Geschlossen
)
- Field-Weighted Citation Impact (FWCI)
- 1.04
- Zuletzt geändert
- 04.03.2026 17:28
