Work

Study on forecasting method of power engineering cost based on BIM and DynGCN

Year: 2025

Type: article

Abstract: In view of the shortcomings of power engineering cost in precision and dynamic in big data environments, this paper proposes building information modelling (BIM) and spatiotemporal modelling-based dynamic graph convolutional neural networks (DynGCN). This study uses the characteristics of BIM technology to carry out the cost management of the whole life cycle of power engineering, and realizes the dynamic control of the cost. In addition, the DynGCN method is used to predict the cost of each engineering link, so as to optimize the construction scheme of the whole project. The results show that the whole life cycle data management supported by BIM technology improves the real-time monitoring and adjustment ability of the cost; the DynGCN method can greatly improve the accuracy of the cost prediction, and the prediction accuracy is 96%, which is closest to the real value of the cost.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0322202

Authors: Huijing Zhai, Jiangtao Ma

Institution: Shandong Electric Power Engineering Consulting Institute Corp

Language: English

Topic: BIM and Construction Integration

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,382