基于卷积神经网络的超高性能混凝土 (UHPC) 压力强度的预测
Li Xu1, Xiaochun Yu2, Chenhui Zhu2
1Jiangsu Vocational College of Business, Nantong 226000, China.
Materials (Basel, Switzerland)
|June 27, 2025
概括
深度学习使用卷积神经网络 (CNN) 准确预测超高性能混凝土 (UHPC) 的压力强度. 这种人工智能模型提供了一种稳定而精确的方法来预测UHPC材料特性.
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.
- 人工智能的人工智能
背景情况:
- 超高性能混凝土 (UHPC) 需要精确的强度预测,用于结构应用.
- 预测混凝土强度的传统方法可能耗时,可能无法捕捉复杂的材料相互作用.
- 深度学习为材料科学中复杂关系的建模提供了一种新的方法.
研究的目的:
- 开发和评估一个卷积神经网络 (CNN) 用于预测UHPC的压力强度.
- 通过使用综合数据集,评估CNN模型的准确性和稳定性.
- 为了比较CNN模型的性能与已建立的机器学习技术.
主要方法:
- 一个双卷积层的CNN架构被设计和实施.
- 该模型在219个UHPC样本的数据集上进行了训练和验证.
- 输入变量包括水泥,水,烟,粉,砂,超可塑剂含量和固化参数.
- 用SHAP (夏普利添加式解释) 分析来确定模型的可解释性.
主要成果:
- 在试验组中,CNN模型实现了0.959的高确定系数 (R^2) 和5.55%的平均绝对百分比误差 (MAPE).
- 美国有线电视新闻网的预测性能与XGBoost相美,XGBoost是表格数据的强大方法.
- SHAP分析证明了CNN模型的可解释性,突出了关键影响因素.
结论:
- 开发的CNN模型对准确预测UHPC压力强度有显著的前景.
- 这种深度学习方法为UHPC的材料设计和质量控制提供了可靠和高效的工具.
- CNN的性能和可解释性表明它有可能在具体的材料科学中得到更广泛的应用.
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