Research On Damage Identification Of High Arch Dam After Earthquake In Considering Forward Analysis Of Prior Information

Most of the high arch dams in China are built in the southwest region.Because of poor geological environment and earthquake,the high arch dams are damaged easily.In order to ensure structural safety,it is important to evaluate accurately the earthquake disaster in high arch dam.A method for damage identification of high arch dam is proposed in where damage induced by earthquake is considered as forward analysis of prior information.The method provides theoretical and technical support for the earthquake damage inversion of high arch dam.Ertan High Arch Dam is taken as the actual engineering background.Its finite element model is established and the actual dynamic characteristics of the structure is verified.The damage of concrete in the dam can be calculated under a set of determined seismic waves.The damage model induced by earthquake of high arch dams can be generalized from the calculation results.The damage model is considered as a priori information which provides necessary constraints for the damage inversion of high arch dams after the earthquake.Due to the priori information,the impact of ill-posedness is reduced.As a priori information is introduced into the damage inversion,a genetic algorithm for screening populations based on prior information is proposed.Firstly,based on prior information,a BP neural network proxy model is used to establish nonlinear relationship between damage parameters and modal parameters.Then,the objective function is constructed with modal parameters,and the population is constructed with damage parameters.The genetic algorithm is used to optimize the objective function for searching the optimal damage parameters.In order to reduce the ill-posedness of damage inversion and avoid local optimal solutions,a priori information is used as a constraint in the traditional genetic algorithm.Before the start of optimization,the initial populations that conform to prior information is generated.While in the optimization process,individuals in the population is selected to satisfy prior information.The results show that the proposed method has better recognition effect and higher noise immunity.Due to the lack of statistical information of the noise in the measured modal parameters,it is difficult to determine the probability distribution function of noise.Then the measured modal parameters have fuzzy features and are the fuzzy variables.So a genetic algorithm based on fuzzy set theory is proposed.First,according to the membership function of the measured modal parameters,the ?-cut set of the measured modal parameters is determined.Then,the objective function is constructed with modal parameters and the population with damage parameters,and the objective function is optimized by genetic algorithm to obtain the optimal damage parameters in the case of ?-cut level.Finally,the membership function of the damage parameters is obtained by combining the recognition results of all the cut sets.In order to reduce the ill-posedness of the damage inversion and avoid the local optimal solution,a priori information is also introduced into the genetic algorithm as a constraint.The results show that the fuzzy set theory can effectively deal with the damage inversion problem of fuzzy variables.The damage parameter membership function identified by the algorithm can effectively guide the structural damage assessment.