3D multiscale characterisation of cement-based building materials

人们对现代建筑材料的要求不断提高。创新解决方案的发展已经产生了大量的粘合剂和混凝土，这些粘合剂和混凝土通常具有非常复杂的、多尺度的异质成分。随着对建筑材料需求的增加，对特性的要求也在增加。结构特征的跨尺度记录仍然是一个未解决的问题，但仍然是中心任务。跨尺度表征一方面提供了对基本建筑材料特性（强度，流体和气体输送，耐久性）的更好理解，另一方面也是现实建模的基础。在提议的项目中，x射线计算机断层扫描（亚微ct）和扫描电子显微镜纳米断层扫描（FIB-REM-nT）的结合将实现混凝土结构的跨尺度3D表示。通过应用最新的图像增强和分析策略（包括机器学习算法），将收集到的数据的评估提升到一个全新的水平。在第一个工作包中，将为均匀样品的两种层析成像方法开发最佳样品制备和数据采集。并行程序应允许在亚微ct体积内注册高分辨率FIB-REM-nT数据。这是改进数据评估的基础，从而通过应用机器学习算法分割比以前更多的阶段。在第二个工作包中，将开发图像配准，分析和分割的算法。例如，为了提高数据质量，在对SEM和CT数据进行去噪时，将一个旋转变压器块集成到编码器-解码器卷积网络UNet中，从而能够通过人工训练数据的合成来建模和去除不同类型的噪声。为了在像素和体素水平上获得高分辨率的分割结果，将重新设计基于变压器的神经网络swan - unet++，以提高微特征识别的预测精度和鲁棒性。在第三个工作包中，将应用开发的程序和算法来表征至少两个砂浆样品的纳米到宏观结构。这是一种标准砂浆和一种孔隙率很低的砂浆。这两个样本将被用作开发和演示现在可用的扩展表征选项的示例，并且这些也将与传统方法进行比较。

The demands on modern building materials are constantly increasing. The development of innovative solutions has produced a multitude of binders and concretes that often have a very complex, multiscale heterogeneous composition. As the demands on building materials increase, so do the demands on characterisation. The cross-scale recording of structural characteristics is still an unsolved, but nevertheless central task. The cross-scale characterisation provides an improved understanding of essential building material properties (strength, fluid and gas transport, durability) on the one hand and is also the basis for realistic modelling on the other. In the proposed project, a combination of X-ray computed tomography (sub-µ-CT) and nanotomography in the scanning electron microscope (FIB-REM-nT) will enable a cross-scale 3D representation of the concrete structure. The evaluation of the collected data will be brought to a completely new level by applying the latest strategies for image enhancement and analysis (including machine learning algorithms). Within the first work package, an optimal sample preparation and data acquisition will be developed for both tomographic methods on uniform samples. The parallel procedure should allow a registration of the high-resolution FIB-REM-nT data within the sub-µ-CT volume. This is the basis to improve the data evaluation and thus segment more phases than possible before by applying machine learning algorithms. In the second work package, algorithms for image registration, analysis and segmentation will be developed. For example, in order to increase data quality, a swin transformer block is to be integrated into the encoder-decoder convolutional network UNet during the denoising of SEM and CT data in order to be able to model and remove different types of noise by means of synthesis of artificial training data. In order to achieve high-resolution segmentation results at the pixel and voxel level, a transformer-based neural network Swin-UNet++ will be redesigned to enable higher predictive accuracy and robustness in micro-feature identification. In the third work package, the developed procedures and algorithms will be applied to characterise the nano- to macrostructure of at least two mortar samples. These are a standard mortar and a mortar with a very low porosity. These two samples will be used as examples to develop and demonstrate the extended characterisation options that are now available, and these will also be compared with conventional methods.