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Application Of Combined Gray Model In Pit Deformation Monitoring Date Processing

Posted on:2013-01-04Degree:MasterType:Thesis
Country:ChinaCandidate:B S HuaFull Text:PDF
GTID:2232330377450269Subject:Photogrammetry and Remote Sensing
Abstract/Summary:PDF Full Text Request
Deep foundation pit construction is a very complicated program geotechnicalproject, it’s also one of the most important research issue. In the process of deepfoundation pit construction, only can we combine the foundation pit supportingstructure, excavation around the soil and adjacent structures comprehensively, thenmonitor them systemly to have a comprehensive understanding of project situation,so that the works will be carried out smoothly.This passage does an analysis on the features of Grey model, moreover, it isbetter than before, which mainly includes:1. In accordance with the guidance of the conventional pit deformationmonitoring technology, we sum up the purpose of the excavation safety monitoring.the monitoring program, monitoring system layout method are ensured according tothe project live, to determine the warning value and monitoring cycle of pitdeformation, so that the base pit monitoring is in a more scientific and reasonablearrangement, which provides a reliable data base for the pit forecast.2. Research on the modeling method of GM (1,1) and conditions apply.Through the analysis of the different orders of GM (1,1) model to predict results, itdetermines the order GM (1,1) prediction model program in this instance. By usingthe Wavelet to transform the initial value of the correction of GM (1,1) model, it canmake the GM (1,1) model forecasts are more reliable. A research on the method ofBP neural network prediction of the timing sequence, it sums up the factors in thepractical application that affect the prediction accuracy of the BP network, including:the hidden nodes and training times, the number of samples, data noise. It alsosummarizes the practical BP network construction method. Using a tandemconstruct tod build the gray-BP network, while taking advantage of the GM (1,1) model fitted values as the input of BP neural network, and there was no predictivevalue higher grayscale.3. to achieve the above model by using the MATLAB, and it is also used in theprediction of deep foundation pit deformation of the two re-Group companies largeforging presses basis and the Pump Station civil construction works to compare withthe experience results, which indicate the practicality and the reliability of theconstructed model.
Keywords/Search Tags:pit deformation prediction, gray model, neural network model, combined model
PDF Full Text Request
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