Seepage Monitoring Model And Safety Alarm Analysis Of Ash Storage Dam

With the development of electric engineering in recent years, the economy is improved. However, various typed dams used in electric engineering have become hidden trouble. The effects of dam breakage are enormous, which will take a serious mischance to the people reside around the dams. It is necessary to establish detection and alarm system of dam depending on modern science and technology in order to provide dam character and reduce dam risk.The paper simulates and predicts real piezometric head of ash storage dam of Jinzhushan electric power plant using BP network and RBF network based on traditional statistical regression model assured model and mixed model which monitors the security of ash dam.The paper advances a new method which makes use of artificial neural network predicting safety coefficient. The method uses artificial neural network to search the relationship between safety factor of dam slope and effect factor, and safety factors are predicted when the factors affecting stability of dam change. The new method is used symmetrical dam and it is proved to the method is feasible. In the final, the method is used to predict safety coefficient of dam stabilization in the project of ash storage dam for Jinzhushan electric power plant.