Study Of Optimal Sensor Placement In Bridge Monitoring Based On Improved Partheno-Genetic Algorithm

In consideration of that a large-scale bridge has the characteristics of huge capital investment, long design cycle and service life, poor working conditions, so it’s necessary to conduct a real-time health monitoring and condition assessment in order to avoid or reduce the loss caused by bridge damage owing to various reasons during its service period. While as one of the key problems of bridge health monitoring system, optimal sensor placement has an essential effect on structural data collection. How to get the most adequate and reliable bridge health information by using sensors as less as possible is the difficulty of optimization approaches for sensor placement.This paper firstly summarizes the development status of bridge health monitoring at home and abroad as well as the common principles and methods of optimal sensor placement. Then based on Partheno-Genetic Algorithm, the self-adaptive genetic operator and multi-objective function are introduced as an improvement, and the improved Partheno-Genetic Algorithm is implemented using the MATLAB programme. Finally, by taking Cross-Zaoqiao Harbor Bridge as an example, the improved Partheno-Genetic Algorithm is successfully used in the optimal sensor placement. And through the above research, several primary achievements are summed up as following:1) Because of its repeal of traditional crossover operator, all of the genetic operation of Partheno-Genetic Algorithm is conducted on a single individual, which can ensure the number of sensors remains unchanged in evolution, while the feasibility and effectiveness of Partheno-Genetic Algorithm are proved through two travelling salesman problems.2) Introducing the self-adaptive genetic operator and multi-objective function as an improvement to Partheno-Genetic Algorithm, not only can improve the performance of the algorithm and prevent premature convergence, but also could calculate several different objective functions, so compared with the Partheno-Genetic Algorithm before improving, the improved Partheno-Genetic Algorithm can better meet the needs of optimal sensor placement.3) Based on Fisher information matrix and modal assurance criterion introduced by cumulative effect, a method of selecting suitable number of modes and sensors is put forward, which can avoid the phenomenon in previous study of optimal sensor placement that the choice of modes and sensors relies mainly on the researcher’s experience.4) By using the improved Partheno-Genetic Algorithm in the optimal sensor placement of Cross-Zaoqiao Harbor Bridge, it comes to the conclusion that the improved Partheno-Genetic Algorithm can well satisfy the economical and functional requirements of sensor system, and has a certain practical value.