基于机器学习的飞灰含量的优化,以改善地质聚合物混凝土的压力强度
Mohammadreza Noori Sichani1, Omid Mazahery Dehkordi1, Morteza Khorshidi2
1Faculty of architecture, Sapienza University of Rome, Rome, Italy.
Scientific reports
|December 30, 2025
概括
这项研究使用人工智能来预测和优化基于飞的地质聚合物混凝土 (FA-GC) 的压力强度 (CS). 图表式先前数据拟合网络 (TabPFN) 模型表现出色,而哈里斯·霍克斯优化 (HHO) 则获得了最高的CS.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 可持续建筑 可持续建筑
背景情况:
- 对可持续建筑材料的需求不断增长.
- 基于飞灰的地质聚合物混凝土 (FA-GC) 为传统混凝土提供了一个有前途的替代方案.
- 需要准确预测和优化FA-GC属性.
研究的目的:
- 使用先进的人工智能模型预测FA-GC的压力强度 (CS).
- 为了优化FA-GC混合设计,以获得最大的性能.
- 确定影响FA-GC压力强度的关键因素.
主要方法:
- 人工智能模型的应用:表格式先前数据拟合网络 (TabPFN),基于直方图的梯度提升 (HistGBoost),M5Prime和自动特征交互学习 (AutoInt).
- 使用Optuna进行超参数调整.
- 使用元启发算法的混合设计优化:哈里斯·霍克斯优化 (HHO),灰狼优化 (GWO),鸟优化算法 (LOA) 和北极熊算法 (PBA).
- 使用SHAP值和部分依赖图的灵敏度分析.
主要成果:
- TabPFN表现出优异的预测准确性 (R2 = 0.981) 和稳定性 (最低的CI和R因子).
- HHO算法实现了最高的压力强度61.56 MPa.
- 确定Al2O3 (%) 和SiO2 (%) 是增强CS最有影响力的成分.
结论:
- 先进的AI模型与元启发式优化相结合,显著改善了FA-GC预测和混合设计.
- 这种方法提高了地质聚合物混凝土解决方案的稳定性和可靠性.
- 这些发现为更高效和可持续的地质聚合物混凝土应用铺平了道路.
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