基于BIM和神经网络的农业节水工程建设成本预测模型
Kun Han1, Tieliang Wang1, Wenhe Liu1
1College of Water Conservancy, Shenyang Agricultural University, Shenyang, 110866, China.
Scientific reports
|July 7, 2025
概括
本研究介绍了一种新的建筑信息建模 (BIM) 和神经网络模型,用于预测农业节水项目成本. BIM-SSA-PGNN模型显著提高了预测的准确性和效率.
科学领域:
- 土木工程 土木工程是指土木工程.
- 建设管理建设管理.
- 人工智能的人工智能
背景情况:
- 农业节水项目面临的挑战包括复杂的条件和高度的不确定性.
- 准确的建筑成本预测对于这些项目的投资决策至关重要.
研究的目的:
- 为农业节水项目开发一个创新的成本预测模型.
- 将建筑信息建模 (BIM) 与神经网络集成,以提高成本估计.
主要方法:
- 利用BIM进行数字化和可视化工程数据.
- 使用粒子图神经网络 (PGNN) 开发了一个预测模型,该神经网络由正弦方形算法 (SSA) 优化.
- 集成的BIM数据与SSA优化的PGNN模型用于使用真实项目数据进行成本预测.
主要成果:
- BIM-SSA-PGNN模型实现了2.99%的最大相对误差.
- 实现了0.1358的根平均平方误差 (RMSE) 和0.9819的R平方 (R2) .
- 与单独的PGNN模型相比,RMSE减少了33%,R2增加了6%.
结论:
- 在农业节水项目中,BIM-SSA-PGNN模型为建筑成本预测提供了卓越的准确性和效率.
- 该研究支持建筑行业向可持续发展的数字化和智能化转型.
- 这种综合方法优化了项目成本,并为管理决策提供了科学依据.
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