High Building Settlement Observation And Prediction

In recent years, some of the high-rise buildings, bridges and other large water conservancy projects come in our lives more and more along with the rapid development and construction business continues to accelerate the modernization process of the country. These tall buildings adds to our city a beautiful landscape, but at the same time there are a lot of security risks. In order to ensure the safety of high-rise buildings, to avoid causing unnecessary economic and personnel losses and casualties, we need do settlement of these observations for the high-rise buildings in accordance with a certain frequency, get settlement data for these high-rise buildings, and analyze the data to be processed, use gray theory prediction model obtain the settlement trend of the high buildings,if do that we can take measures to avoid the occurrence of disasters timely, and can ensure the safety of high-rise buildings.Generally, the settlement observation data of buildings often contain some settlement abnormal data.In this paper,we use VB software, use limit of error detection method and the t-test for detection of the settlement observation data, see if there are abnormal data. If there are some abnormal data, software will be corrected these,then use the gray model to predict the size of the building. In this paper, Qiyuan building settlement observation as an example in Xuancheng City, first we use14monitoring points of settlement building to the times of12to analyze the data then map, then use the gray model to predict the size of the building. Papers in predictive analysis process, first use of VB program selected three subsidence monitoring points do the detection,if there are abnormal data then the program can do correction for these data. Gray model must require data such as intervals, So before settlement prediction we should use polynomial fitting intervals ranging data into interval data, then use VB software to predict, that can get subsidence data of next time. The accuracy of prediction models to reach Ok and achieve the intended purpose.