基于ISSA-BPNN-AdaBoost的混凝土压力强度的预测
Ping Li1, Zichen Zhang1, Jiming Gu1
1School of Management Science and Engineering, Anhui University of Technology, Ma'anshan 243002, China.
Materials (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了一种改进的机器学习模型,用于预测混凝土的压力强度,减少对昂贵的物理实验的依赖. 这种新的方法提高了准确性,并展示了集体学习在建筑材料科学中的力量.
科学领域:
- 土木工程 土木工程是指土木工程.
- 材料科学 材料科学 材料科学
- 计算智能是一种计算智能.
背景情况:
- 传统的混凝土强度测试是耗时且昂贵的.
- 机器学习为准确的混凝土强度预测提供了一个有希望的替代方案.
- 优化集体学习模型是提高预测准确性的关键.
研究的目的:
- 开发一个高度准确的混凝土压力强度预测模型.
- 通过优化基础学习者来提高集合学习模型的性能.
- 为优化神经网络模型引入改进的子搜索算法 (ISSA).
主要方法:
- 开发了一种改进的乌搜索算法 (ISSA),增强了人口多样性和搜索功能.
- 使用ISSA优化了一个后传神经网络 (BPNN),作为基础学习器.
- 一个自适应增强 (AdaBoost) 算法集成了多个ISSA-BPNN模型来预测混凝土的压力强度.
主要成果:
- 改进的子搜索算法 (ISSA) 在测试函数上的优化算法相比,表现优越.
- 在两个数据集上,ISSA-BPNN-AdaBoost模型在混凝土压力强度预测方面取得了很好的结果.
- 拟议的模型在准确性方面明显优于其他整体和单个模型.
结论:
- ISSA-BPNN-AdaBoost模型准确地预测了混凝土的压力强度,为物理测试提供了具有成本效益和效率的替代方案.
- 合体学习,特别是优化基础学习者,显示出对具体强度预测的显著优越性.
- 该研究强调了先进的机器学习技术在土木工程应用中的潜力.
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