| After bridges having been constructed and opened to traffic, their material will be deteriorated or aged gradually because the influence of the weather, environmental factors and their strength and stiffness will degrade with the time running for the action of the static and active loads applying on them. Not only will this endanger the safety of the traffic, but also it will shorten the life span of the bridge. Apparently, for bridges with a complex layout and large scales, it' s difficult to hold all the information that can reflect the condition of the bridges globally and more difficult to evaluate their safety factors and tracks of deterioration systematically by the traditional ways of visual bridge inspection, maintenance or local inspection. Therefore, to assure the security and durability of the bridges, it is necessary to construct a health monitoring system that can provide all the information reflecting the condition of the bridges globally at any time needed.Nowadays, Structural Health Monitoring Systems have been installed on many long span bridges permanently. However, they do not have the ability of automatic damage detection because of no FEM model existing. Based on the achievements of the forerunners, a model of the Bridge Health Monitoring System is suggested by using Genetic Algorithms and Neural Network in different parts of the system on the basis of their characters while dealing with complex nonlinear problems. Therefore, the following research work has been carried out in this doctoral dissertation.1. In the research of the existing bridges, some problems need to be solved immediately such as how to evaluate the condition of a bridge globally, which is the best procedure to evaluate the strength of a bridge, how to evaluate the residual life of a bridge, how to maintain a deteriorated bridge and how to carry out an economy analysis. During the regular inspection and maintenance, the most important thing is to evaluate the global condition of a bridge correctly and timely. Taking< as a reference and making use of the Orthogonal Design Method and the Uniform Design Method in statistics, a forecast model is suggested to evaluate the global condition of a bridge based on Neural Network.2. Combined with some approximate modal information and response of the bridges in their static or active mode, a procedure for damage detection of bridges is suggested based on Neural Network because of its stronger mode clustering ability. On the other way, combined with some methods of parameter identification based on strain or displacement information coming from static measurement, another procedure for damage detection of bridges is suggested based on Genetic Algorithms because of its strong nonlinear searching ability.3. By identifying the load condition of a bridge with the information of strain and displacements from the Bridge Monitoring System stored on it, one can, first, replenish and check the information from the monitoring system, and second, provide information for the damage detection in the next step. The key point of load identification is to model the relation between the load and the deflection caused by load correctly and quickly. Utilizing the data from a research on the nonlinear behavior of a partially prestressed concrete beams controlled by displacement as samples and taking the loads applied on the beams and the deflection at the mid-span as input and output for the model respectively, a model of load and deflection is suggested and used to identify the load condition of concrete bridges based on Neural Network in the dissertation and the results are satisfactory.4. Crack width is one of the most important indexes to indicate the reliability and durability of a partially prestressed concrete bridge. It is very important to precisely model the relationship between the crack width and the applied load of a partially prestressed concrete beam. However, the process of the formation and development of the cracks on a concr...
|
PDF Full Text Request