Study On Cost Forecasting Of Steel Structure Residence Based On GA-WNN

With the development of national economy and the acceleration of population’s moving into the cities, Chinese housing constructing develops with unprecedented speed. But these great successes in construction are cost with high energy consumption and high pollution. At the same time, steel output in our country grows considerably, which provides enough raw materials preparation for building structural steels, a new type of residential building system—the steel structure housing in China begins to get more and more attention for research and development. Steel construction residence possesses some advantages such as high industrialized rate, less wet working, less pollution, being recycled, light weight, short construction period, the comprehensive low price, which conform to the trend of the development of housing industrialization. Steel construction residence just gets started up in our country. The study of the related theory for steel construction residence is of great significance. This paper researches the cost forecast of Steel construction residence, the main content are as follows:Firstly, this paper analyzes the research background and its significance, and summarizes domestic and international current situation of the steel construction residence and project cost forecast. Secondly, analyzing the technology and economy of steel construction residence, and summarizing the advantages of the steel construction residence, this provides beneficial basis to the implementation and development in china for steel construction residence. Thirdly, by using the literature review method, the index of scholars researching the project cost forecast is retrospective analyzed, the number of available indexes in the literature is choose and the steel construction residence cost forecast indexes is also selected combined with the characteristics of the steel construction residence, and then choose10index incorporated into five comprehensive index according to the partial order, which as the in put of the neural network, and then the engineering cost forecast index system is established. Finally, using genetic algorithm to optimize the wavelet neural network weights, combined with the selected forecast index, based on GA-WNN steel construction residence cost forecast model is established to forecast the engineering sample.