The Technique Applies In The Aspects Of Bridge Damage Detection And Recognition

With the continuous development of bridge construction, functions and formats of bridge become more complicated day by day. The technology of repairing and strengthening of bridge has been emphasized. 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 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 and claimed the lives of people. Therefore, detection and inspection of bridges have become one important guarantee that ensures safety maintenance and normal use of it. So, how to perform quality detection and safety inspection with bridges has become a research hotshot of foreign academia and engineering.View of the various hidden trouble of the in-service bridges exist, this paper presents some methods on crack fault diagnosis. We should learn the performance and the security situation of the bridge at any time, and avoid the catastrophic accidents happened. The main work and achievement are summarized as follows:1. Application of genetic algorithms and neural networks in detection technique.We establish the artificial neural networks model to appraise the steel bar corrosion degree and chose some parameters which sensitive to the structure damage as the input vector of the network. This method takes the damaged structure condition as the output vector of the network, and uses the Genetic Algorithm optimization the network weight and threshold value. The main idea is establishing a damage training sample sets, and carries on the training to the network, then forecast structure extent of damage.2. Local positioning of crack fault based on deflection information. Owing to the emergence of crack, some of the characteristic parameters of bridges will cause the irregular change or mutation, these parameters contains important information of the crack. Structural cracks can cause the appearance of the local stress concentration and the mutation of the deflection information in the same region of space domain. Therefore, we set up a neural network model which chooses the deflection information of cantilever beam under the static load and the relative positions of the crack as the input and output vector respectively. It is showed that the proposed method is feasible to diagnose the crack fault of structure.3. A method of crack fault diagnosis based MEP (multi-expression programming) and frequencies contour line is discussed. In this method, the MEP model is trained by the cracks diagnostic parameters which are the inherent frequencies obtained by ANSYS. Based on the independent variables, the relative position and the relative depth of crack, the frequency surface is drawn. By taking the first three inherent frequencies of structure as input parameters (of the frequency surface), Contour Lines of frequencies is drawn. According to the intersection point of frequency contour lines and the forecasted results of MEP, the structural crack position is located. The results show that the method can be an effective integration of both advantages and achieve the rapid and accurate location of the crack.