Research on engineering cost prediction based on GA-BP neural network

In order to improve the accuracy of engineering cost prediction and reduce prediction errors, an engineering cost prediction method based on GA-BP neural network is proposed in this paper. Comprehensive index system for engineering cost prediction is constructed, and qualitative indicators are discretised using the equal interval method. The qualitative indicators are transformed into quantitative indicators through scale assignment. The BP neural network error is obtained through gradient descent, and the GA algorithm is used to adjust the weights from the output layer to the hidden layer. Using the discretised qualitative indicators as input vectors and engineering cost as the output vector, a prediction model for engineering cost based on GA-BP neural network is built to obtain prediction results. Experimental results show that the proposed method has a prediction range of 2.41%, a residual mean range of 0.005~0.219, a recall rate fluctuating between 96.9% and 99.7%, and high prediction accuracy.