3D multiscale characterisation of cement-based building materials

人们对现代建材的需求在不断增加。创新解决方案的开发产生了大量粘结剂和混凝土，这些粘结剂和混凝土往往具有非常复杂的、多尺度的不同组成。随着对建筑材料的需求增加，对特性的要求也在增加。结构特征的跨尺度记录仍然是一项尚未解决的任务，但仍是中心任务。跨尺度表征一方面提供了对基本建筑材料属性(强度、流体和气体传输、耐久性)的更好理解，另一方面也是逼真建模的基础。在拟议的项目中，扫描电子显微镜(FIB-REM-NT)中的X射线计算机层析成像(SUB-µ-CT)和纳米层析成像技术的结合将使混凝土结构的跨尺度三维表示成为可能。通过应用最新的图像增强和分析策略(包括机器学习算法)，对收集的数据的评估将提升到一个全新的水平。在第一个工作包内，将为均匀样品上的两种层析方法开发最佳的样品制备和数据采集。并行程序应允许在亚微CT体积内记录高分辨率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来分割比以前可能的更多的阶段。在第二个工作包中，将开发图像配准、分析和分割算法。例如，为了提高数据质量，在对扫描电子显微镜和CT数据进行去噪期间，将SWIN变换块集成到编解码器卷积网络NET中，以便能够通过合成人工训练数据来对不同类型的噪声进行建模和去除。为了获得像素和体素级别的高分辨率分割结果，将重新设计基于变压器的神经网络Swin-UNET++，以在微观特征识别中实现更高的预测精度和稳健性。在第三个工作包中，开发的程序和算法将用于表征至少两个砂浆样品的纳米到宏观结构。这些是标准砂浆和孔隙率非常低的砂浆。这两个样本将作为例子来开发和演示目前可用的扩展表征选项，并将这些选项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.