Predicting multiple properties of concrete using empirical models has become increasingly challenging due to the complexity of modern concrete formulations and the nonlinear behavior of their constituents. This study introduces a sequential model that integrates mix proportions with microstructural information of concrete. The model addresses the limitations of small datasets and the inherent variability in concrete's raw materials and production processes. A novel dataset comprising concrete mix proportions, 56,160 scanning electron microscope images, and their corresponding macroscopic properties was constructed for training and validation. We developed a sequential model integrating a Swin Transformer (Swin-T) with a Back Propagation Neural Network (BPNN), achieving superior accuracy in predicting compressive strength and permeability. Comprehensive evaluations using SHAP and GradCAM reveal the critical role of hydration products in these predictions, underscoring the enhanced interpretability and efficacy of our approach. This work advocates for the integration of microstructural insights to improve the reliability and precision of concrete assessments.

中文翻译：

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基于微观结构的深度学习模型，用于准确预测多种混凝土特性


由于现代混凝土公式的复杂性及其成分的非线性行为，使用经验模型预测混凝土的多种特性变得越来越具有挑战性。本研究引入了一个顺序模型，该模型将混合比例与混凝土的微观结构信息集成在一起。该模型解决了小数据集的局限性以及混凝土原材料和生产过程的固有可变性。构建了一个包含混凝土配合比、56,160 张扫描电子显微镜图像及其相应宏观特性的新数据集，用于训练和验证。我们开发了一个顺序模型，将 Swin Transformer （Swin-T） 与反向传播神经网络 （BPNN） 集成在一起，在预测抗压强度和磁导率方面实现了卓越的准确性。使用 SHAP 和 GradCAM 进行的综合评估揭示了水合产品在这些预测中的关键作用，强调了我们方法增强的可解释性和有效性。这项工作倡导整合微观结构见解，以提高混凝土评估的可靠性和精度。