Processing of building subsidence monitoring data based on fusion Kalman filtering algorithm

Subsidence monitoring is an important means to ensure the safety of buildings. After subsidence monitoring, it is necessary to mine key information from the monitored information, interpret the subsidence through in-depth analysis of the information, and predict the subsidence of buildings, especially the residential buildings. To improve the accuracy of subsidence interpretation and prediction, this paper applies the tracking fusion Kalman filtering algorithm to process the building subsidence monitoring data. Tracking fusion algorithm is a global suboptimal weighted state fusion algorithm. This algorithm not only can significantly improve the local estimation error, but also has the advantages of small computational burden and good fault tolerance, which is very convenient for practical engineering application. The simulation results show that the proposed tracking fusion Kalman filtering algorithm outperformed the local Kalman filtering algorithm adopted by Deng et al. in estimation accuracy of building subsidence. The research results provide reference for the protection of building safety. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

This study proposed a tracking fusion Kalman filtering algorithm for processing building subsidence monitoring data, aiming to improve the accuracy of subsidence interpretation and prediction, providing a reference for building safety protection.