The Study of the Usage of Cost-Significant Theory and Neural Network in Project Cost Estimation

Based on the reference to domestic and foreign correlative theories and methods, this paper uses the model of cost estimation based on cost-significant theory and neural network theory to estimate project cost. Firstly, the cost-significant theory is put forward to solve the tedious operation issues by finding out significant items to simplify the operational difficulty of engineering cost estimation. Then the back-propagation neural network model is made up according to the BP neural network to "distill" CSIs and csf (cost- significant factor)from the data and information of the completed projects, which provides a practical solution for those problems according to the nonlinear theory. The basic theories of BP neural network and CS are introduced and their applications are illustrated with an example . From the example, we can find that the relative errors are so small that they can meet the accurate demand of cost estimation after simulation. And the test result shows that the model based on cost-significant theory and neural network theory is accurate and successful.