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Damage Identification Of Cable-Stayed Bridge Based On Cable Force And Strain Monitoring

Posted on:2021-04-07Degree:MasterType:Thesis
Country:ChinaCandidate:W L QinFull Text:PDF
GTID:2392330602993870Subject:Road and Railway Engineering
Abstract/Summary:PDF Full Text Request
The rapid economic growth has led to the rapid development of transportation,and long-span bridges play an increasingly important role in transportation.China is one of the countries with the fastest economic development in the world,and also one of the countries with the largest number of long-span bridges.The service life of long-span bridge structure is up to several decades or even hundreds of years.The coupling effect of disaster factors such as environmental erosion,material aging,long-term effect of load,fatigue effect and catastrophe effect will inevitably lead to damage accumulation and resistance attenuation of structure and system,so as to reduce the ability to resist natural disasters and even normal environmental effects.In extreme cases,it will lead to disaster An unexpected accident.It is difficult to meet the requirements of ensuring the safe operation of the structure only by the previous intermittent detection and evaluation.Therefore,the long-term health monitoring system of bridge structure has developed rapidly in the past 20 years.How to use the response information collected by the health monitoring system of the bridge structure to reflect the working state of the bridge structure,how to use the data of the health monitoring system in depth and how to arrange the sensors reasonably are the key problems to realize the damage identification and state evaluation of the long-span bridge structure.In this paper,based on the actual project of Cangkou cable-stayed bridge of Qingdao Bay Bridge,the damage identification of cable-stayed bridge structure based on long-term monitoring is preliminarily discussed by means of real bridge monitoring and numerical simulation.The main research contents are as follows:(1)Taking Cangkou cable-stayed bridge of Qingdao Bay Bridge as the engineering background,the finite element model is established for structural monitoring analysis and damage identification,and the model is modified with the health monitoring results of the actual bridge as the goal;the distribution characteristics of static cable forces are analyzed based on the actual monitoring cable forces throughout the year.The damage simulation is carried out on the modified finite element model,and then the change rule of static cable force and its damage sensitivity under the damage state are studied.(2)Based on the change characteristics of static cable force distribution,and according to the symmetry,the cable-stayed bridge is divided into four regions;the damage identification vector is constructed by the change of static cable force mean value in the region,and the damage identification of single damage,combined damage of two kinds of damage and the number of damage in the region is carried out based on the modal confidence criterion.(3)For the damage identification of the whole bridge,firstly,based on the resolution analysis of the damage condition of the whole bridge based on the cable force,select the cable for the whole bridge,so as to achieve a better identification effect of all the damage conditions with less monitoring cable force;then use the same method as the above regional damage identification to identify the single damage and combined damage of the whole bridge.(4)Due to the wide spatial distribution of stay cables,the advantage of damage identification based on cable force is that it is conducive to the large-scale damage location.But the sensitivity of cable force to local small damage is low.For this reason,the multi-scale model is used to further study the local small damage identification of steel box girder based on the monitoring strain.A multi-scale model is established for the main segments of steel box girder,and the local small damage identification based on strain monitoring and neural network method is studied.
Keywords/Search Tags:damage identification, monitoring cable force, damage vector, multi-scale model, BP neural network
PDF Full Text Request
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