An Improved Real Coded Genetic Algorithm And It's Application In Structural Damage Detection

The detection and identification of structural damage is a vital part of the monitoring and servicing of structural systems during their lifetime. Structural damage in normal service may include corrosion, fatigue, and aging, or it may be caused by impact loads, earthquakes, and wind. Damage in engineering structures is caused by natural disasters, the fatigue of the structures and so on. The accumulation of damage caused by these different reasons will lead the structure wreck or reduce the structure performance. If the damage can be identified as early as possible, and the structure can be repaired in time, it is obviously that a lot of maintenance costs can be saved, and unnecessary loss of human life and property can be avoided and reduced. So, the on-line health monitoring and diagnosis of engineering structures is a popular field which attract lots of attention from domestic and abroad researchers.It is absolutely that occurrence of damage leads to changes in the dynamic properties of the structure. Vibration-based damage identification techniques have received significant attention in academe and engineering fields.(1) First, the theory and methodology of the structural damage detection are expounded in the paper, the research status in quo from home and abroad is also introduced. In this paper, the inhesion relationship between modal parameters ,such as frequency,mode shape, and the damage is discussed. The existed damage detection methods are classified into a few categories and the characteristics of these methods are analyzed;(2) A systemic research about the basic theory and implement technique of the standard genetic algorithm is made, in succession, an in-depth study about the limitation and shortage of the standard genetic algorithm is made. And also the improve approach of the algorithm is analyzed. Based on these analyses an amelioration strategy is proposed, an index which scale the population diversity is defined, and this index is imported into the genetic operator, and it is used to instruct the crossover probability and mutation probability to tune self-adaptively. And an improved self-adaptive parallel genetic algorithm based on real coding is proposed, and some typical test functions are optimized with the proposed method, the results show that the algorithm proposed in this paper is an effective global optimization algorithm.(3) This damage detection method based on the improved genetic algorithm proposed in this paper was employed to identify the damage of a frame model simulated with MATLAB. The results show that this method proposed in this paper can identify the location of the damaged elements and the extent of their damage, and this method is noise robust. It is also proved that we can use the limited modal parameters to identify the damage with a high precision.(4) In order prove the effectiveness of the proposed algorithm in practical engineering project, a set of parameter identifications was conducted on a four-story reinforced concrete frame structural model with the scale of 1:3 under the laboratory environment. Different from the numerical simulation, we can't accurately judge whether the identification result is right. Therefore, pertinence analysis between the analytical modal of the updated structure and the actual modal is used to judge whether the result is right.(5)This damage detection method based on the improved genetic algorithm proposed in this paper was employed to analyze Benchmark model structure proposed by the International Association for Structural Control and the American Society of Civil Engineers (IASC-ASCE) Task Group on Structural Health Monitoring. The results show that, the damage situation can be determined exactly by the method proposed in this paper. So this method is effective and the diagnosis results are satisfactory.(6) The damage detection method based on the improved genetic algorithm proposed in this paper was also employed to identify the damage of beam-structure. First the proposed algorithm was used to identify prestressed beams under the laboratory environment, and then the damage detection was performed to a experimental aluminum cantilever beam. The results show that our algorithm is effective in damage detection for beam-structure.