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Project Cost Prediction for Building Complexes Based on Grey BP Neural Network Model

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DOI: 10.23977/cpcs.2022.060201 | Downloads: 3 | Views: 139

Author(s)

Haochuan Jia 1

Affiliation(s)

1 Zhejiang College of Security Technology, Wenzhou, Zhejiang, 325016, China

Corresponding Author

Haochuan Jia

ABSTRACT

Engineering cost prediction is a common topic in the construction field, but there is a problem of large prediction error. In order to avoid investment loss, a method of project cost prediction for building complex based on gray BP neural network model is designed. The construction area is divided into two factors: above-ground construction area and underground construction area, the cost influencing factors of the construction project are extracted, the density of data points at the center of the construction point is defined, the pricing model of the construction group project is optimized, the cost of similar projects is projected, and the cost prediction method is designed using the gray BP neural network model. Experimental results: The average values of relative errors of the designed construction group project cost prediction method and the other two construction group project cost prediction methods are: 3.596%, 6.505% and 6.213% respectively, indicating that the designed construction group project cost prediction method is more practical after combining with the gray BP neural network model.

KEYWORDS

Grey BP neural network, Building complex engineering, Cost prediction, Renovation project cost, Project management, Project quality

CITE THIS PAPER

Haochuan Jia, Project Cost Prediction for Building Complexes Based on Grey BP Neural Network Model. Computing, Performance and Communication Systems (2022) Vol. 6: 1-9. DOI: http://dx.doi.org/10.23977/cpcs.2022.060201.

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