Data Disposal and Analysis with Least Squares Collocation in Tall Building Health Monitoring

As more and more high-rise buildings are constructed in cities in China, the monitoring, evaluation and maintenance are involved in simultaneously during tall building operation and management. Further more, the different data process and analysis results of health monitoring of tall building can create different maintenance and management disposal methods, and which conduct important influence on evaluation of safety, stability and future using. Traditional theories and methods of data processing commonly used in regression curve and multiple linear regressions etc. But there only can be used for dealing with similar observations, on the other hand, the gross error of data has no good approach to analysis and calculate in high precise. In this paper, a tall building deformation was monitored through precise leveling between datum mark and subsidence point. The subsidence analysis method based on least squares collocation was used during data disposal process. With Gauss Function to simulate covariance matrix of observation data in different monitoring points, the optimal subsidence and incline rate were calculated. Then the stability and safety of tall building can be evaluated in a short period or medium-term, make decisions for maintenance and management.