建筑结构学报2024，Vol.45Issue(3)：101-112,12.DOI:10.14006/j.jzjgxb.2023.0031
多元特征驱动的超高层建筑变形状态智能学习与预测
Multi-feature driven intelligent learning and prediction of deformation state of super-tall buildings
摘要
Abstract
To predict the future state of structures using monitoring data,an intelligent learning and prediction method for estimating the deformation state of super-tall buildings driven by multiple features is proposed.Through self-adaptive signal decomposition,multi-dimensional analysis of response characteristics and sub-signal correlation analysis,structural response data reconstruction and combination are realized,and multi-channel learning and multi-step prediction are performed based on long short-term memory network.Using the measured data of the horizontal displacement response at the top of the Shanghai Tower under the influence of Typhoon Muifa,it is revealed that the displacement response data have superimposed characteristics of ultra-low-frequency quasi-static deformation,fundamental modal control vibration response and non-stationary characteristics in the time domain.Through reconstruction of the response data,three sub-signal combinations with different time scales,vibration amplitudes and stationary characteristics are formed.The displacement response of the next 1-60 time steps are successfully predicted based on 300 time steps of monitoring data via three independent LSTM models.The results show that under the condition of controlling the future prediction time step(in 60 time steps),the data-driven learning prediction model proposed in this study can fully learn and predict the known displacement response data characteristics and physical state with excellent accuracy,the normalization error can be controlled within 10％.The proposed method can predict the deformation state of super-tall buildings accurately in real time.关键词
超高层建筑/结构健康监测/变形状态/LSTM模型/时序数据Key words
super-tall building/structural health monitoring/deformation state/LSTM model/time series data分类
建筑与水利引用本文复制引用
单伽锃,张茜,吕西林,张其林..多元特征驱动的超高层建筑变形状态智能学习与预测[J].建筑结构学报,2024,45(3):101-112,12.基金项目
国家自然科学基金项目(51878483,52278312),上海市期智研究院科技合作项目(SYXF0120020109). （51878483,52278312）
