水利学报2024，Vol.55Issue(8)：966-976,989,12.DOI:10.13243/j.cnki.slxb.20240014
基于虚拟传感器的坝区多输出自由场地震时程长序列预测模型研究
Research on multi-output seismic time-history long-term sequences prediction model for free field of dam based on virtual sensors
摘要
Abstract
The multidimensional long-term prediction of seismic time-history in dam areas holds significant im-portance for rapid damage analysis.Virtual sensors,as complementary sensing mechanisms to seismic physical sensors,facilitate seismic time-history predictions.However,existing virtual sensors face challenges in effec-tively predicting long-term sequences for multiple signals,leading to delays in analyzing dam seismic damage.Addressing the aforementioned issue,a multi-output seismic time-history long-term sequences prediction model based on TFA-Seq2Seq virtual sensors is proposed.This model enhances the Seq2Seq virtual sensors using multi-task learning,restructuring them into an"Encoder-3 Decoder"architecture.This structure establishes the map-ping relationship between multiple dam physical sensor signals and long-term seismic time-history in three free-field directions.Additionally,an attention mechanism is integrated to capture temporal dependencies among mul-tiple input signals,resolving synchronous multi-output prediction issues and enhancing prediction accuracy.Fur-thermore,Time-Frequency transform(TF)layers and their inverse transformation layers are introduced to improve the Encoder and Decoder,shortening the temporal length of seismic signals and extracting frequency domain fea-tures.Correspondingly,a model training strategy involving stochastic forced learning is proposed to overcome the limitations of existing virtual sensors in effectively predicting long sequences.Case studies demonstrate that the proposed method achieves a virtual sense of 10 seconds ahead for seismic signals in three directions within dam free field.Compared to models without attention mechanisms and single outputs,the proposed method exhibits an enhanced prediction accuracy of 6.88％and 3.32％,respectively.This research presents novel insights and ap-proaches for advancing the anticipatory sense of seismic information during seismic events.关键词
自由场地震/虚拟传感器/多输出长时序预测/TFA-Seq2Seq/多任务学习Key words
free field seismic/virtual sensors/multi-output long-term sequences prediction/TFA-Seq2Seq/multi-task learning分类
建筑与水利引用本文复制引用
苏哲,刘宗显,余红玲,佟大威,余佳,王晓玲..基于虚拟传感器的坝区多输出自由场地震时程长序列预测模型研究[J].水利学报,2024,55(8):966-976,989,12.基金项目
天津市自然科学基金项目(22JCQNJC01150) （22JCQNJC01150）
