Study On Degradation Prediction Of Conditon Of Prestressed Concrete Girder Bridges

By the end of 2016,the number of national highway bridges has reached 805,300 and 49,169,700 meters.With the external environmental factors and the continuous increase in service time,the conditions of the bridges in the road network have been degraded to varying degrees.In order to eliminate the occurrence of bridge safety accidents,it is necessary to manage the service bridges scientifically and systematically.The degradation prediction of the bridge conditions has become one of the core functional modules in the bridge management system and has become the focus of current bridge management research.Prestressed concrete girder bridges are the most numerous types of bridges in highway networks.The prediction of their conditions has important practical significance.Firstly this paper introduces three commonly used bridge condition prediction models and compares the advantages and disadvantages of each.And then selects the most appropriate model for the prediction of project-level and network-level bridge condition.Finally uses the prestressed concrete girder bridge as the instance background which applies and tests the optimized model.The main research content of this article includes the following points:1.The theoretical foundations of the three commonly used bridge condition prediction models,namely the regression analysis prediction model,the grey system theory prediction model and the Markov prediction model,are introduced in detail.The corresponding bridge condition prediction models are established respectively.The advantages and disadvantages and the prediction accuracy of the three models are compared and analyzed,which lays the foundation for the later model optimization research.2.Aiming at the degradation prediction of project-level bridges,the paper proposes a combination of the two methods based on the advantages and disadvantages of the grey system theory and Markov theory,and optimizes them.First,the level eigenvalues are introduced to optimize the prediction values of the Markov mode.Then the order clustering method is used to optimize the division of state space.Finally,the concept of error is introduced to simulate the random fluctuations in the process of bridge degradation.An improved gray-Marco prediction model can be obtained through the above methods.3.Based on the Markov theory,the optimization of the degraded prediction of network-level bridge condition is studied.Firstly,various factors that affect the forecasting results of Markov forecasting model are analyzed.Then combined with the actual characteristics of network-level bridge degradation,the method of solving the Markov transition matrices is improved.Finally,from the factors influencing bridge degeneration,optimized transition matrices which change with the influencing factors are obtained.Based on the above optimization method,an optimized Markov prediction model is obtained.4.The project-level optimization model and the network-level optimization model are applied to the condition prediction of prestressed concrete girder bridges.On the one hand,based on project-level forecasting,an improved Gray-Markov prediction example model was established.The grey system theory prediction model,the traditional GrayMarkov prediction model and the improved Grey-Markov prediction model were compared and analyzed.The prediction results show that the prediction accuracy of the improved Gray-Markov prediction model is the highest.On the other hand,for the network-level forecasting,the application of the optimized Markov prediction model in the network-level bridge condition is introduced.According to the results comparison,the prediction accuracy of optimized Markov prediction model is higher than that of general Markov prediction model.