Open AccessResearch on Construction Cost Estimation based on Artificial Intelligence Technology
Author(s) -
Bin Wang,
Jianjun Yuan,
Kayhan Zrar Ghafoor
Publication year - 2021
Publication title -
scalable computing. practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.192
H-Index - 18
ISSN - 1895-1767
DOI - 10.12694/scpe.v22i2.1868
Subject(s) - artificial neural network , gray (unit) , cost estimate , toolbox , computer science , estimation , matlab , artificial intelligence , data mining , machine learning , industrial engineering , operations research , engineering , systems engineering , medicine , radiology , programming language , operating system
For the prediction of economic expenses involved in construction industry, cost estimation has become an important aspect of construction management for the prediction of economic expenses and successful completion of the construction work. Cost analysis is crucial and require expertise for accurate and comprehensive estimation. In order to effectively improve the accuracy of construction project cost, this paper establishes an estimation model based on gray BP neural network. It combines the MATLAB toolbox for program design, and learns and tests the input and output of training samples. This article determines the application of grey system theory to optimize the estimation model of Back Propagation (BP) neural network. The viability of the method established in this article, is tested by collecting the engineering cost data in Zhengzhou city and comparing between the standard BP neural network and the gray BP neural network methods. The results show that the average error of the gray system theory optimized BP neural network model designed in this paper is 2.33%. The gray BP neural network model studied in this paper can not only quickly estimate the project cost, but also has high accuracy rate. The outcomes obtained establishes a model with scientific and reasonable construction project cost estimation.