Engineering Cost Prediction Model Based on DNN

Abstract

A DNN-based cost prediction method is proposed for the difficult problem of cost calculation in engineering cost accounting, combined with deep neural networks. Firstly, we introduce the basic information of artificial neural network and select the DNN structure to calculate the engineering cost price according to the characteristics of the data related to engineering cost price. Secondly, the DNN-based engineering cost price prediction model is constructed, and the two types of index systems, engineering characteristics and list item characteristics, are used as model inputs. In addition, the total quotation and each subitem engineering quotation and tax are used as model outputs by analyzing previous relevant studies. Based on this, simulation experiments are conducted on the DNN-based engineering cost price prediction model, and it is concluded from the training model that the DNN model has a better prediction effect. Among them, the relative error of total price forecast by DNN is 4.203%, and the relative error of integrated unit prices V1 and V2 is 2.98% and 4.52%, respectively, with small relative error. Finally, by reasonably adjusting the integrated unit price, the cost price of the integrated unit price and the cost price of the total offer can be calculated.

Analysis and prediction of railway track longitudinal level using multiple machine learning methods

“…However, the ANN model is easy to overfit and has low training speed. Compared to the ANN model, the deep neural network (DNN) model has better nonlinear processing capability due to more hidden layers and an optimised training algorithm, which can handle mathematical and physical problems with larger datasets and more complex features and mostly obtain a higher prediction accuracy [43]. Therefore, the DNN model has become popular.…”