Load Model, Based On A Combination Of Gray Markov Chain Forecasting Random Bridge

The scale of construction of transport infrastructure is determined by the traffic volume of bridge load random process. The rationality and reliability of the traffic volume prediction will have a direct impact on investment and effectiveness of highway projects. The development strategy of future highway transport, rational use of resources and utilizing efficiency of highway transport facilities are of great significance for the development of future transport, which provides a basis of future transport development strategy. According to the findings of random load, the status of traffic volume growth is determined, and the bridge load random process prediction model and random effect model are proposed. The main research works are as follows:1,The principle of traffic volume prediction is elaborated, the basic theory of grey prediction and markov chain are researched, and the grey prediction steps is proposed in this dissertation. Respectively, the full information of grey forecasting model, grey prediction model of new information, grey metabolic prediction model and grey prediction model for residual principle are analyzed and contrasted. According to the definition and nature of the markov chain, the important C-K equations are proposed.2,Improved grey prediction model ideas of the bridge load random process is proposed. Respectively, the improved full information prediction model, new information prediction model, metabolic prediction model and residual prediction models are established. Using the traffic volume data of a station in Guizhou province, the accuracy of the model is improved, and the future traffic volume of the station is predicted.3,It overcomes deficiencies of the grey prediction method, and proposes the improved grey Markov chain prediction model for the traffic volume of bridge load random process. The curve of the grey prediction model reflects the development trend of the historical traffic volume, and Markov chain reflects the impact of traffic volume by random volatility, and this method takes into account the trend and fluctuation factors to predict the outcome, so it overcomes the traffic volume limitations of the traffic prediction by the role of a single prediction model. Combined with the actual situation, the traffic volume is predicted accurately and comprehensively.4,The vehicle load of highway bridges belongs to a kind of timely variable load, highway bridge load prediction model is proposed in this dissertation. Based on several domestic typical highway traffic measured data over the past few years, the prediction model can predicts the load effect number of different vehicle types, it also can be used to count for in-service reinforced concrete bridges fatigue load. It has an important and guiding significance to make fatigue assessment.