Study On Grey-markov Chain Model Based On Wavelet Transforming And Its Engineering Application

In recent years, with the accelerating of modernization of our country, constructionbusiness has been rapidly developing, in result more and more high-rise buildings, large-scalewater conservancy, bridge construction and other large buildings have erected in our lives.Needless to say, these large buildings have brought a lot of convenience to our lives, but italso hides a lot of security issues. In order to avoid building economic losses and casualtiescaused by security problems, it requires regular monitoring of the deformation of the buildingto get the settlement deformation data. By processing and analyzing the acquired data, we canpredict the trend of building deformation with the use of certain prediction model, and taketimely and appropriate measures to ensure the safety of the building as well as prevent orreduce disasters.In general, building settlement observation data is a series of short sequences of discretedata, which contains noise and volatile. With analyzing the various methods of processing andforecasting deformation monitoring data and combining with the characteristics of buildingsettlement based on existing research, the paper put forwards the gray Markov chain modelbased on wavelet transforming, and apply it in building subsidence deformation monitoringdata processing and forecasting. The paper takes the settlement and deformation monitoringdata of4thbuilding of Xi’an Hongxin International Garden as an example, selects a number ofrepresentative monitoring points and analyzes with the application of the method proposedabove. The main works and contributions are as follows: with the use of wavelet de-noisingprogram compiled under MATLAB to realizing wavelet decomposition and waveletreconstruction, get useful de-noised data at first; then, establish adaptive weighted grayforecasting model on the data de-noised and predict the sedimentation value as well ascalculate the relative error of predicted and observed values, and then divide state fromrelative error with the use of Markov chain model and determine the future of the settlementamount state range so as to predict settlement valuesof the building; At last, compare theresults obtained from different methods of Gray Model, Gray Model based on WaveletTransforming and Gray-Markov Model based on Wavelet Transforming. Comparative resultsshow that the Wavelet Transforming Gray-Markov model is superior to the simple GrayModel and the Wavelet Transforming Gray Model, which means the prediction accuracy ofthe results can be further improved. For data with large volatility, the superiority of theWavelet Transforming Gray-Markov model is particularly evident, which provides a newmethod of data processing and forecasting for random data sequence.