Construction cost prediction model for agricultural water conservancy engineering based on BIM and neural network.

ABSTRACT: Due to the complex construction conditions, long work cycles, and high uncertainty inherent in agricultural water conservancy projects, accurate construction cost prediction is crucial for investment decisions. This study presents an innovative cost prediction model for these projects, integrating BIM with neural networks. Firstly, BIM technology is utilized to digitize and visualize engineering-related information. Subsequently, a prediction model based on SSA optimized PGNN is constructed. The digital data obtained from BIM is subsequently integrated with the prediction model to estimate the construction costs of agricultural water conservancy projects. In this study, actual engineering projects are selected as case studies, utilizing material price data from January 2016 to February 202

SUBMITTER: Han K 

PROVIDER: S-EPMC12234848 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature