使用可解释的机器学习预测可持续增材增强混凝土中的关键裂传播长度
Manish Kewalramani1, Arsalan Mahmoodzadeh2, Raouf Hassan3
1Department of Civil Engineering, College of Engineering, Abu Dhabi University, Abu Dhabi, UAE.
Scientific reports
|December 14, 2025
概括
预测混凝土裂传播现在更准确,使用一种新的机器学习方法. 神经接触核高斯过程 (NTK-GP) 模型提供可靠的预测,确定纤维类型和体积作为关键因素.
科学领域:
- 材料科学与工程 材料科学与工程
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
背景情况:
- 预测可持续添加剂增强混凝土 (SAEC) 中的临界裂传播长度 (CCPL) 对结构耐用性至关重要.
- 现有的CCPL预测实验和数值方法通常是复杂的,昂贵的和计算效率低下的.
- 需要准确,高效和数据驱动的方法来预测SAEC中的CCPL.
研究的目的:
- 开发和评估一个全面的机器学习 (ML) 框架,用于预测SAEC的CCPL.
- 为了比较集体,基于内核和深度学习模型的性能,重点关注一个新的神经触点内核高斯过程 (NTK-GP) 模型.
- 确定影响CCPL的关键特征,并为预测提供不确定性量化.
主要方法:
- 开发了一个ML框架,集成集体,基于内核 (NTK-GP) 和深度学习模型.
- 使用800个SAEC样本的数据集,具有9个关键特征,经过受控实验测试.
- 使用统计指数 (R2,RMSE,MAPE,VAF) 和交叉验证评估模型性能;用于特征重要性和相互作用分析的可解释人工智能 (SHAP);用于不确定性量化.
主要成果:
- 该NTK-GP模型显示出优异的预测性能 (R2=0.950.96,RMSE=0.740.90毫米,MAPE=0.090.14,VAF=0.950.96).
- 可解释的AI分析确定了纤维类型 (FT) 和纤维体积含量 (FVC) 作为最有影响力的特征 (>65%的差异),具有显著的相互作用.
- 统计测试证实了NTK-GP与最先进模型的可比性,并且引导间隔验证了预测可靠性.
结论:
- 拟议的ML框架,特别是NTK-GP模型,为SAEC中CCPL预测提供了一个准确,高效和可解释的替代方案.
- 该研究强调了纤维类型和体积对裂传播的重大影响,为材料设计提供了洞察力.
- 这种以数据为导向的方法推进了结构耐久性分析,并支持开发可持续的,耐破裂的混凝土材料.
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