| 注册
首页|期刊导航|建筑结构学报|基于物理信息增强的高层结构地震反应智能重构与预测

基于物理信息增强的高层结构地震反应智能重构与预测

王律己 单伽锃 赵鹏

建筑结构学报2026,Vol.47Issue(4):50-64,15.
建筑结构学报2026,Vol.47Issue(4):50-64,15.DOI:10.14006/j.jzjgxb.2025.0436

基于物理信息增强的高层结构地震反应智能重构与预测

Physics-enhanced intelligent reconstruction and prediction of seismic responses in high-rise buildings

王律己 1单伽锃 2赵鹏3

作者信息

  • 1. 同济大学 土木工程学院,上海 200092
  • 2. 同济大学 土木工程学院,上海 200092||上海韧性城市与智能防灾工程技术研究中心,上海 200092
  • 3. 上海地震局,上海 200062
  • 折叠

摘要

Abstract

High-rise buildings,as critical carriers of urban functionality,inevitably experience structural performance degradation during long-term service,posing challenges for accurate seismic performance assessment.Structural health monitoring(SHM)provides an essential means to capture in-service structural conditions.However,its effectiveness is often constrained by sparse measurements and missing data,which hinder comprehensive full-field response evaluation.Consequently,there is an urgent need for methods capable of reconstructing and predicting structural seismic spatio-temporal fields under such incomplete observation conditions.To address this issue,this study proposed a physics-enhanced framework for multi-step spatiotemporal prediction of seismic responses in high-rise buildings.The framework represented floors as graph nodes,constructed adjacency matrices based on identified mode shapes and inter-story dynamic coupling,and employed a spatiotemporal interaction graph neural network to enable response reconstruction and multi-step prediction under sparse observations.Meanwhile,the hyperparameters sensitivity analysis was performed to reveal the correlations between the hyperparameters with the prediction accuracy.The proposed method was systematically validated using the Millikan Library at the California Institute of Technology,USA,and a landmark supertall building in Shanghai,China.Results demonstrate that the framework accurately reconstructs responses at uninstrumented floors and effectively captures the propagation of vibration information across nodes in both time and frequency domains.Across multiple scenarios,the normalized root-mean-square error remains below 5%,highlighting the high prediction accuracy and strong engineering applicability of the proposed approach.

关键词

高层建筑/时空响应/深度学习/智能重构/地震反应

Key words

high-rise building/spatiotemporal response/deep learning/intelligent reconstruction/seismic response

分类

天文与地球科学

引用本文复制引用

王律己,单伽锃,赵鹏..基于物理信息增强的高层结构地震反应智能重构与预测[J].建筑结构学报,2026,47(4):50-64,15.

基金项目

国家自然科学基金项目(52278312,52422810),中央高校基本科研业务费专项资金(22120250133). (52278312,52422810)

建筑结构学报

1000-6869

访问量1
|
下载量0
段落导航相关论文