| Popis: |
The inaccuracy of cost estimation in feasibility study and design stage is one of main reasons that make investment of railway construction projects out of control in China. In those two stages cost estimations are affected by many uncertain factors, and the relationship among them is nonlinear and traditional model is hard to solve them. This paper applies CS (Cost-Significant) theorem to simplify the cost estimation, and sets up the forecasting methods for CSIs (Cost-significant Items) of WLC (Whole Life Costing) . Then the BPNN (Back-propagation Neural Network) model is made up according to BP algorithm to "distill" CSIs from the Plethora of the data and information of performed projects, which provides a practical solution for those problems according to the nonlinear theory. Then the FIS (Fuzzy Inference System) is applied to estimate the CSIs and csf(cost-significant factor) with no performed data. The basic theories of BPNN, CS , FISand WLC are introduced and their applications are illustrated with an example in this paper. From the example, we can see that the relative errors are small enough for accuracy demand of cost estimations after simulation, and test result shows that the model based on CSIs, WLC and BPNN theorems is accurate and successful. |
