In this paper, we present a methodology for measuring the reaction degree of ground granulated blast furnace slag (GGBFS) in alkali-activated cements using neural network based image analysis. The new methodology consists of an image analysis routine in which the segmentation of the back scattered electron (BSE) (SEM) images is based on a deep learning U-net. This methodology was applied to and developed for NaOH-activated slag cements and validated against independently measured XRD results. In a next step the developed method was applied to NaOH-Na₂SO₄-activated systems, to check the broader applicability. The neural networks based image analysis results were shown to correlate well with the XRD results. Once the model was trained, it segmented images fast and accurately. Furthermore, the model trained on the NaOH-activated systems was readily applicable on NaOH-Na₂SO₄-activated system indicating that the model generalises well. As such, the developed methodology and models can be more performant and robust than conventional threshold-based image segmentation. The method's accuracy, replicability and transferability make it a promising tool for material analysis and characterisation.

中文翻译：

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基于深度学习的电子显微图像分析量化碱活化水泥中矿渣反应程度的方法

在本文中，我们提出了一种使用基于神经网络的图像分析测量碱活化水泥中磨碎粒状高炉渣 (GGBFS) 反应程度的方法。新方法包括一个图像分析例程，其中背散射电子 (BSE) (SEM) 图像的分割基于深度学习 U-net。该方法应用于 NaOH 活化矿渣水泥并对其进行了开发，并针对独立测量的 XRD 结果进行了验证。在下一步中，将开发的方法应用于 NaOH-Na ₂ SO ₄- 激活系统，以检查更广泛的适用性。基于神经网络的图像分析结果显示出与 XRD 结果良好相关。模型训练完成后，它可以快速准确地分割图像。此外，在 NaOH 活化系统上训练的模型很容易适用于 NaOH-Na ₂ SO ₄活化系统，表明该模型具有很好的泛化性。因此，所开发的方法和模型可以比传统的基于阈值的图像分割更具性能和鲁棒性。该方法的准确性、可复制性和可转移性使其成为材料分析和表征的有前途的工具。