摘要
不均匀沉降会对历史建筑产生严重危害,因此对历史建筑物不均匀沉降的预测十分必要。回归分析模型是经典的预测模型,但它过于依赖旧数据,无法处理实时监测数据,在实际工程中存在诸多不便。通过建立卡尔曼滤波模型,对上海某历史建筑在基础托换期间的沉降监测数据进行滤波和预测,同时基于卡尔曼滤波数据进行多项式回归预测,并和传统的多项式回归分析模型进行预测对比分析。结果表明:卡尔曼滤波能够很好地预测历史建筑的不均匀沉降,而且卡尔曼滤波可以剔除数据中的随机扰动,提高预测精度。
Uneven settlement will cause serious damage to historical buildings.Therefore,it is necessary to pre-dict the uneven settlement for historical buildings.Regression analysis model is a classical prediction model,but it is too much dependent on old data and can’t deal with real-time monitoring data.It is very inconvenient in engineering.In this paper,Kalman filter model is applied to predict the settlement of a historical building in Shanghai during foundation replacement.At the same time,a polynomial regression model based on filter values is built.The result is compared with the traditional polynomial regression model.It shows that Kalman filter can not only predict the uneven settlement of historical buildings,but also eliminate random disturbances in the data.So it can improve prediction accuracy.
作者
陈小杰
龚赛博
CHEN Xiao-jie;GONG Sai-bo(Shanghai Housing Quality Inspection Station,Shanghai 200031,China;School of Environment and Architecture,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《桂林理工大学学报》
CAS
北大核心
2021年第2期350-353,共4页
Journal of Guilin University of Technology
基金
国家自然科学基金项目(51208300)
国家重点研究发展计划项目(2016YFC0802405)。
关键词
历史建筑
卡尔曼滤波
回归分析
沉降预测
不均匀沉降
historical buildings
Kalman filter
regression analysis
settlement prediction
uneven settlement
