使用基于模糊逻辑的建模评估高性能混凝土的硬度特性
Qian Li1,2,3
1Beijing University of Technology, Beijing, 100022, China. liqiannew@163.com.
Scientific reports
|September 29, 2025
概括
一个新的混合型模糊人工神经网络 (HFANN) 改善了对高性能混凝土 (HPC) 性能,如压力强度和沉降的预测. 这种先进的模型比建筑工程应用中的传统方法提供了更好的准确性.
科学领域:
- 建筑工程工程 建筑工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 准确预测高性能混凝土 (HPC) 的性能 (压力强度,沉降) 对于有效的建筑工程至关重要.
- 复杂的,非线性关系和水-水泥比率和添加剂等因素的数据变化挑战了传统的建模方法.
- 现有的方法往往无法充分解决预测具体表现所固有的不确定性.
研究的目的:
- 引入一种新的混合模糊人工神经网络 (HFANN) 架构,以增强对HPC属性的预测.
- 将模糊逻辑与机器学习模型 (SVR,MLP,GBM) 集成,并通过混乱游戏优化 (CGO) 进行优化.
- 提高HPC混合设计和可持续建设预测模型的准确性和稳定性.
主要方法:
- 开发了一种混合模糊人工神经网络 (HFANN),将模糊逻辑 (高斯成员函数,Sugeno类型规则) 与SVR,MLP和GBM集成在一起.
- 利用混乱游戏优化 (CGO) 通过混乱搜索进行超参数和集体重量优化.
- 预处理的输入使用模糊逻辑生成18模糊的特征每样本,捕捉数据不确定性.
主要成果:
- HFANN框架表现出卓越的预测准确性,实现压力强度的根平均平方误差 (RMSE) 减少25%.
- 在训练,验证和测试数据集中实现了高的R2值:压力强度为0.98和沉降为0.97-0.99.
- MANOVA和Tukey的HSD测试证实了与传统预测方法相比的显著改进.
结论:
- 拟议的HFANN框架为预测关键HPC属性提供了一个强大而准确的工具.
- 这种方法有效地处理混凝土混合设计中的复杂关系和不确定性.
- 通过提高HPC混合设计效率,HFANN模型促进了可持续建筑实践.
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