Abstract：Resarch purposs ： The transport infrastructure is in a large - scale，high - standard construction period at present in China. However，the investment management is incompatible with the increasing of government investment. The main reason for these problems is the existing cost estimation model which mostly use linear method，and the actual project cost can’t be classified as simple linear properties due to various factors and differences. This paper combined the whole life cost - significant theory with artificial neural networks，studied and explored the reliability and accuracy of nonlinear methods for investment project cost. Resarch conclusions ：( 1) Using cost - significant method in the whole life cycle cost can quickly accurately find out the actual data，greatly improve the efficiency and accuracy. (2) Through contrasting the prediction precision of WLCS and CS，the method of Hopfield neural network can improve the calculation precision，simplify the calculation process， and reduce the computation time. Hopfield model can quickly realize the associative memory of significant project，and accurately estimate the budget of the proposed construction project cost. ( 3 ) The estimation method and evaluation system can be applied to cost estimation and control in the field of infrastructure projects.
段晓晨,吕倩,张小平. 投资项目WLCS的Hopfield预测模型研究[J]. 铁道工程学报, 2016, 33(3): 96-101.
DUAN Xiao - chen1，LV Qian1，ZHANG Xiao - ping2. Research on the Estimating Whole Life Cost - significant ofConstruction Projects Based on the Hopfield Neural Network. Journal of Railway Engineering Society, 2016, 33(3): 96-101.
2016, Vol. 33 
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