Research On The Application Of BP Neutral Network Based On Particle Swarm Optimi Zation In Dam Displacement Prediction

As China's economy boom in the in the recent three decades after opening-up and reform, the water power engineering construetion develops at a high speed, the body of dams beeomes huger and higher, and the safety of dams also has taken more and more attention. Dam deformation prediction is an important part of dam safety monitoring system, and plays a very important role in safeguarding the seeurity of dam.Dam deformation prediction is based on known monitoring data to prediction the future deformation. The internal structure and the working condition of the dam is complex, and there are various uncertainty factors,these factors can't be described by certain ration relation in traditional models. It is hard to quantitatively determine the relationship of these factors and dam deformation. Therefore, this paper applys BP Neural Network, which has organization capability, self-educated capability, adapt capability and fuzzy ratiocinative capability, in the filed of dam deformation prediction, uses its nonlinear function approaeh ability to simulate the nonlinear relationship of the deformation of the dam and the influencing factors. But during the using of BP Neural Network in dam deformation predictioning, we find some shortcoming. So, we must take some methods to improve performance of the BP Neural Network.Initialized weights and threshold of the BP neural network is random, which results in slow convergence and easily convergence to local optima. According to these characteristics, this paper applys the Particle Swarm Optimization (PSO), which has a strong global searching ability, to optimize the weights and threshold of the BP neural network. This paper utilizes the transverse displacement monitoring data of Fengman Dam, establishs a PSO-BP model and applys MATLAB to simulate it, and then contrast the result with classic BP neural network mode. Results show that PSO-BP model is faster in training and more prediction accurate.In addition, the paper attempts to prediction the dam deformation range, which is more in line with the actual deformation in theory, establishs an appropriate PSO-BP neural network prediction model of the dam deformation range and applys MATLAB to simulate it. Through the analysis of the prediction results, we get a conclusion that the PSO-BP neural network model is feasible to prediction the dam deformation range.