通过使用深度学习技术,评估高性能混凝土的强度特性,以合体和混合模型的形式进行评估
Zhe Wang1,2, Tao Sun3, Yan Sun4
1National Center for Nanoscience and Technology, Beijing, 100190, China. hahawojiaowangzhe@126.com.
Scientific reports
|July 14, 2025
概括
这项研究开发了先进的混合和组合模型,以准确预测高性能混凝土 (HPC) 的压力强度和落流量. 基于GWO的GBQP模型在预测混凝土压力强度方面表现出卓越的性能.
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
- 计算科学 计算科学
背景情况:
- 混凝土的压力强度 (CS) 受各种组件的影响,如聚合物,粘合剂和添加剂.
- 高性能混凝土 (HPC) 使用诸如飞灰和烟等添加剂,通过液压或pozzolanic活动来增强性能.
- 对HPC属性的准确预测对于计算分析和材料设计至关重要.
研究的目的:
- 开发和评估混合和整体深度学习模型,用于预测高性能混凝土 (HPC) 的压力强度 (CS) 和倾斜流量.
- 调查各种预测模型的有效性,包括T-SFIS,GBMBoost和决策树,与元启发算法 (GWO,QPSO) 集成.
- 通过敏感性分析 (SHAP) 和十倍交叉验证来评估模型性能.
主要方法:
- 开发混合和集体深度学习框架,将基础模型 (T-SFIS,GBMBoost,决策树) 与元启发优化器 (GWO,QPSO) 结合起来.
- 使用了一个包含191个高性能混凝土混合物的数据集.
- 通过R2 (R2),根平均平方误差 (RMSE),SHAP灵敏度分析和十倍交叉验证进行性能评估.
主要成果:
- 基于GWO的GBQP模型实现了压力强度预测的最高精度 (R2=0.998,RMSE=1.216 MPa).
- 整体DGT模型显示了CS预测的第二个最佳性能.
- 对于流预测,TSQP模型表现最好 (R2=0.984,RMSE=3.233mm),紧随其后的是GBQP.
结论:
- 混合和整体深度学习模型为预测HPC压力强度和落流量提供了高度准确和高效的方法.
- 超启发式优化显著提高了深度学习模型对具体属性的预测能力.
- 这些先进的计算技术是优化HPC混合设计和性能分析的宝贵工具.
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