Status quo and Existing Problems of Seismic Prevention Planning in Counties and Districts——a Case Study of Hongsibao District, Wuzhong City,Ningxia Hui Autonomous Region

This paper introduced domestic and overseas status, the historical evolution and the development history of earthquake disaster reduction planning. The urgencies and realistic significance were analyzed to carry out earthquake disaster reduction planning for districts or countries in the northwest. With Hongsibao District in Wuzhong City, Ningxia Province as an example, this article analyzed in detail the present situation and the special problems of earthquake disaster reduction planning in Northwest China. The relevant solving measures were put forward in order to offer a reference for the scientific establishment and effective implementation of earth- quake disaster reduction planning in Northwest China. The foundation of earthquake disaster reduction in the Hongsibao District is still very weak, there is a single earthquake monitoring means, the emergency rescue system is not complete, the working mechanism is not perfect, and the lack of funding for the work of earthquake disaster reduction.

A Quantitative Seismic Topographic Effect Prediction Method Based upon BP Neural Network Algorithm and FEM Simulation

Topography can strongly affect ground motion,and studies of the quantification of hill surfaces’topographic effect are relatively rare.In this paper,a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method(FEM)was developed.The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation,but the correlation between PGA amplification factors and slope is not obvious for low hills.New BP neural network models were established for the prediction of amplification factors of PGA and response spectra.Two kinds of input variables’combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra,respectively.The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors,and they can be mostly within 0.2 for response spectra’s amplification factors.One input variables’combination can achieve better prediction performance while the other one has better expandability of the predictive region.Particularly,the BP models only employ one hidden layer with about a hundred nodes,which makes it efficient for training.