An Improved Convolution Neural Network-Based Fast Estimation Method for Construction Project Cost

Abstract

At present, most network models do not fully consider the prior information on construction project cost, and do not do much research to find out more information or effectively utilize the information the find on construction project cost; conversely, some previous networks mainly consider the depth of the network for the reconstruction results. In addition, there is no detailed discussion on the selection of loss functions for various algorithms, and some common error loss functions are directly used. In view of these shortcomings, a fast estimation model of construction cost based on an improved convolutional neural network is proposed. The construction project is decomposed according to the project characteristics. The project characteristic factors that have a greater impact on the project cost and the main project quantity are selected and analyzed. Then, the convolutional neural network principle is used to analyze the project characteristic factors, the project cost and main project quantities. We simulate and compare the established convolutional neural network models, verify the usability of the model, and finally get a model with high reliability. The establishment of this model provides a new method for the estimation of project cost and main economic indicators, which can meet the actual work requirements to a certain extent.