用深度学习自动分割纤维混凝土3D图像中的裂缝，提升结构评估效率。
Segmentation of cracks in 3d images of fiber reinforced concrete using deep learning
- 基于3D U-Net模型，结合真实混凝土的模拟裂缝数据训练。
- 在多种纤维混凝土样本中实现高精度裂缝分割，准确识别内部裂纹结构。
- 适合材料力学、无损检测领域研究人员，尤其关注混凝土耐久性分析者。
混凝土结构中的裂缝普遍存在，是此类非均质材料的重要特征。标准测试产生的裂缝特性可提供关于混凝土配比及其力学性能的宝贵信息。仅观察表面裂缝会遗漏大量内部结构信息。计算机断层扫描（CT）可在不破坏微结构的前提下探测样品内部。重建后的断层图像为3D体素图像，体素灰度值反映局部X射线吸收强度。为识别属于裂缝的体素并完成裂缝结构分割，需开发合适的算法。卷积神经网络在具备充足且一致训练数据时能有效解决此类任务。本文采用经典的U-Net的3D版本，并在其上训练了半合成的3D图像数据，这些数据基于真实混凝土样本并嵌入模拟裂缝结构。本文阐述了整体方法流程，并展示如何使网络能够检测不同类型的实测混凝土3D图像中的真实裂缝系统，特别是纤维增强混凝土。
原文摘要 · Abstract (English)
Cracks in concrete structures are very common and are an integral part of this heterogeneous material. Characteristics of cracks induced by standardized tests yield valuable information about the tested concrete formulation and its mechanical properties. Observing cracks on the surface of the concrete structure leaves a wealth of structural information unused. Computed tomography enables looking into the sample without interfering or destroying the microstructure. The reconstructed tomographic images are 3d images, consisting of voxels whose gray values represent local X-ray absorption. In order to identify voxels belonging to the crack, so to segment the crack structure in the images, appropriate algorithms need to be developed. Convolutional neural networks are known to solve this type of task very well given enough and consistent training data. We adapted a 3d version of the well-known U-Net and trained it on semi-synthetic 3d images of real concrete samples equipped with simulated crack structures. Here, we explain the general approach. Moreover, we show how to teach the network to detect also real crack systems in 3d images of varying types of real concrete, in particular of fiber reinforced concrete.
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