基于随机森林的建筑成本预测系统,由鸟算法优化
Zhishan Zheng1, Lin Zhou2, Han Wu3
1School of Big data and Computer, Jiangxi University of Engineering, Xinyu 338000, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
精确的施工成本预测是使用一种新的随机森林模型优化了鸟算法改进. 这种方法提高了复杂项目的预测准确性和效率.
科学领域:
- 建设管理建设管理.
- 预测建模预测建模
- 在工程领域的人工智能.
背景情况:
- 建筑成本预测面临诸多挑战,包括精度低,效率差以及由于项目的复杂性而造成的高度不确定性.
- 现有的方法经常与建筑成本因素的动态和多方面的性质作斗争.
研究的目的:
- 开发一个先进的预测指数系统和一个强大的预测模型,用于建设成本.
- 通过解决传统方法的局限性,提高建筑成本预测的准确性和效率.
主要方法:
- 确定影响建筑成本的关键因素,并开发一个14指数预测系统.
- 构建一个随机森林 (RF) 预测模型,使用鸟算法 (BSA) 为参数调整进行优化.
- 使用来自中国建筑公司的工程数据进行验证,将BSA与其他优化算法和RF与其他预测方法进行比较.
主要成果:
- 拟议模型的最大相对误差仅为1.24%,符合工程实践要求.
- 与其他元启发算法相比,鸟算法证明了与最佳参数的融合速度更快.
- 在准确性和效率方面,RF-BSA模型超过了传统和先进的预测方法.
结论:
- 开发的预测模型为建筑成本预测提供了显著的改进.
- 通过更可靠的预测,这些发现为优化建筑项目的成本管理提供了基础.
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