Evidence map›Paper›PMID 40624081›Full record

ArticleScientific reports2025

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

Kun Han, Tieliang Wang, Wenhe Liu, Chunsheng Li, Xiaochen Xian, Yingying Yang

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Kun HanCollege of Water Conservancy, Shenyang Agricultural University, Shenyang, 110866, China.
Tieliang WangCollege of Water Conservancy, Shenyang Agricultural University, Shenyang, 110866, China.
Wenhe LiuCollege of Water Conservancy, Shenyang Agricultural University, Shenyang, 110866, China.
Chunsheng LiCollege of Water Conservancy, Shenyang Agricultural University, Shenyang, 110866, China. 496954285@qq.com.
Xiaochen XianLiaoning Agricultural Development Service Center, Shenyang, 110030, China.
Yingying YangLiaoning Agricultural Development Service Center, Shenyang, 110030, China.

Funding

Liaoning Province Local Standard Project Approval Plan 2024223、2024224、2024225
6 · The paper itself

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 2021 in Liaoning Province, along with real project data for modeling purposes. The results indicate that the maximum relative error between the predicted and actual values of the combined model is only 2.99%. Furthermore, the RMSE and R

Indexed as

Agricultural water conservancy engineeringBIM technologyConstruction costGrey BP neural network (PGNN)Sparrow search algorithm (SSA)

Identifiers

PMID40624081
PMCPMC12234848

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.