Research On Several Problems In Deformation Data Processing, Analysis And Prediction

Deformation analysis and deformation trend prediction not only has important significance inthe aspects of engineering construction and ensure of people's life and property safety, but also isa complex systematic engineering, even if purely speaking from the angle of technical theory. Incompany with the update of deformation monitoring technology and the requests of engineeringpractice, this research has focused on how to introduce advanced mathematical theories andanalytic methods to give an insight into the nonlinearity and complexity of deformation.This paper has outspreaded around each step of deformation monitoring data processing.Following are research fmdings and specific contents.(1) This paper has systematically summed up the theory and computational procedure ofmonitoring network adjustment using the method of appending datum, expatiated advantages ofreplacement of classical control network adjustment with this method, investigated and perfectedthe adjustment theory and methodology of the method of appending datum when consideringerrors in initial numerical data. Besides, this paper has deduced concrete computation formulasand checked out it using examples.(2) In view of the request that nonlinear monitoring network adjustment can be used in nonlinearsystem, this paper has indepth researched the nonlinear global optimation characteristics ofgenetic algorithm and effects of different genetic operator combinations to the computationalconvergence rate, proposed a method to confirm the optimal genetic operator combination.Morever, this paper has put forward substitution of the crossover operation with simplex method,shaping a hybrid genetic algorithm which can get higher convergence rate and computationalaccuracy. Besides, using trial functions and examples of nonlinear control network adjustment,this paper has verified the advantages of hybrid genetic algorithm compare to traditional onewhen used in nonlinear optimation.(3) The important difference between modern deformation data analysis and traditionaldeformation data processing lies in the request for the establishment of dynamic and nonlinearprediction models. Aiming at defects exist in artificial neural network which is a dynamic andnonlinear model method, this paper has studied using genetic algorithm to modify neural network algorithm, that is to say using genetic algorithm to optimize network parameters. Examples haveproved that the modified algorithm has good learning capacity, which can approach trainingsamples with high accuracy and can get good predictive validity.(4) Kalman filter has particular advantages when used to weaken the affects of noise inmonitoring data and predict the future state of a system. According that both time serried andKalman filter are dynamic model methods which are established on recursion modal, this paperhas deduced the interconversion relationship between them, and put forward that an innovationmodel of time series analysis using observation data can be utilized when statistical informationof noise is absent. Then the prediction gain matrix and filter gain matrix can be deduced usingmodel parameters. After that, Kalman filter model and prediction model can be established.Consequently, both noise statistical information and complex Riccati formula computation whichare required in the traditional Kalman filter are no longer necessary in the new model.