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在超高性能混凝土中基于机器学习的裂口开口位移的预测
Arsalan Mahmoodzadeh1, Manish Kewalramani2, Abdulaziz Alghamdi3
1Center of Research and Strategic Studies, Lebanese French University, Erbil, Iraq.
Scientific reports
|November 14, 2025
概括
现在可以更准确地预测纤维增强超高性能混凝土 (FR-UHPC) 的裂口开口位移. 表格式先前数据配套网络 (TabPFN) 有效地建模复杂的关系,确定光纤体积和长度作为弹性结构设计的关键因素.
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
- 计算力学 计算力学 计算力学
背景情况:
- 超高性能混凝土 (UHPC) 为关键基础设施提供了卓越的强度和耐用性.
- 由于材料的非线性行为,预测纤维增强UHPC (FR-UHPC) 中的裂口开口位移 (CMOD) 是复杂的.
- 现有的模型很难捕捉混合设计,纤维特性和影响CMOD的结构参数的复杂相互作用.
研究的目的:
- 在FR-UHPC中开发一个准确和可解释的CMOD预测模型.
- 确定控制FR-UHPC断裂行为的关键材料和几何参数.
- 利用先进的机器学习来增强结构设计和优化UHPC应用程序.
主要方法:
- 为FR-UHPC组建了一个包含11个混合物和材料变量的综合实验数据库.
- 通过五重交叉验证,训练和评估了九个机器学习算法,包括表式先前数据拟合网络 (TabPFN).
- 采用了夏普利添加式解释 (SHAP) 和其他可解释性技术来分析模型预测和特征重要性.
主要成果:
- 纤维体积 (FV) 和纤维长度 (FL) 被确定为影响CMOD的主要因素.
- 特性选择通过关注六个最具影响力的变量来优化模型.
- TabPFN实现了卓越的预测准确性 (R2 = 0.942,RMSE < 0.072 mm),超过了其他先进的算法.
- 解释性分析证实了FV和FL的意义,二次影响来自烟,飞灰,隙深度和水与粘合物比率.
结论:
- TabPFN提供了一种非常准确和可靠的方法来预测FR-UHPC中的CMOD,其中包括不确定性量化.
- 开发的框架为设计和优化弹性UHPC结构提供了实用工具.
- 这项研究开创了TabPFN用于CMOD预测的应用,并将可解释性与数据驱动断裂力学中的不确定性估计相结合.
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