Simulation of high-precision cost prediction method for the entire process of construction engineering
In order to improve the accuracy of cost prediction throughout the entire construction project and en-sure effective cost control,an improved neural network-based method for predicting the cost of the entire construction project process was proposed.Based on systems engineering,a standard system for the cost of the entire construction project process was constructed at first.On the basis of the standard system,the grey relational analysis method was employed to calculate the weight of each cost prediction index,and then the index data with bigger weights were re-tained as training samples.Next,the AdaBoost algorithm was introduced to improve the neural network.Moreover,a strong predictor was set up to train the samples,thus achieving high-precision cost prediction for the entire construc-tion project process.Simulation results show that the proposed method can effectively select the cost prediction indexes with greater impact on construction project cost management,while achieving high-cost prediction accuracy and training fitting degree.Therefore,this method can improve the reliability of prediction.
NETL
NSTL
万方数据
