使用最先进的机器学习技术预测纤维增强超高性能混凝土 (FR-UHPC) 的碳化深度
Arsalan Mahmoodzadeh1, Raouf Hassan2, Nejib Ghazouani3
1Center of Research and Strategic Studies, Lebanese French University, Erbil, Iraq.
Scientific reports
|January 6, 2026
概括
一个新的数据驱动框架预测了纤维增强超高性能混凝土 (FR-UHPC) 的碳化深度. 先进的机器学习,包括基于人工智能的回归管道搜索 (AIPSR),实现了高精度,改善了耐用性预测.
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:
- 碳化是混凝土结构退化的主要原因之一.
- 由于其复杂性,现有的模型与纤维增强超高性能混凝土 (FR-UHPC) 斗争.
- 对FR-UHPC碳化的预测模型是有限的.
研究的目的:
- 开发一个数据驱动的框架来预测FR-UHPC碳化深度.
- 应用先进的机器学习技术,以提高耐用性评估.
- 为了提高碳化预测模型的解释性.
主要方法:
- 使用了800个实验点的数据集.
- 采用了机器学习技术:用于建模物理系统的神经运算符 (NOMPS),基于人工智能的回归管道搜索 (AIPSR),量子机器学习 (QML) 和使用量子Shapley值 (EAIQSV) 的可解释AI.
- 使用统计分析和5倍交叉验证的验证模型.
主要成果:
- 确定了固化时间,温度和二氧化烟雾含量作为影响碳化深度的关键因素.
- 与其他模型相比,AIPSR显示出更高的预测准确性 (R2 = 0.83) 和一致性.
- 该框架为FR-UHPC提供了可靠和可重复的碳化预测.
结论:
- 开发的框架为预测FR-UHPC碳化提供了一个准确和可解释的方法.
- 纳入量子启发的机器学习增强了预测能力.
- 这种方法有助于评估和提高混凝土结构的耐用性.
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