A Neural Network Model for Expressway Investment Risk Evaluation and its Application

Investment risk evaluation of expressways plays a significant role in the sustainable development of expressways. A consideration of the inconsistency between operation accuracy and operation efficiency of the traditional quasi-3-layer BP neural network evaluation model is first established. This paper proposes and designs a variable-structure neural network model in the random re-linking process from the input layer neurons to the hidden layer neurons. Then, the model acquires the hidden layer to the output layer from the angle of structural optimization. Secondly, in view of the characteristics of the Chinese expressway investment, this paper develops an expressway project investment risk evaluation index system. Furthermore, a design of the expressway project investment risk evaluation model is completed based on the variable-structure neural network of the re-linking random process. In addition, the model has been verified with ten Chinese expressway projects. The risk evaluations have been conducted for four of the ten expressway projects. The research result shows that the average relative error predicted with such model is 1.91% and the maximum relative error is 2.63%. Therefore, the prediction result is deemed suitable.