Author:
Jung Christian,Redenbach Claudia
Abstract
AbstractAs the number one building material, concrete is of fundamental importance in civil engineering. Understanding its failure mechanisms is essential for designing sustainable buildings and infrastructure. Micro-computed tomography (μCT) is a well-established tool for virtually assessing crack initiation and propagation in concrete. The reconstructed 3d images can be examined via techniques from the fields of classical image processing and machine learning. Ground truths are a prerequisite for an objective evaluation of crack segmentation methods. Furthermore, they are necessary for training machine learning models. However, manual annotation of large 3d concrete images is not feasible. To tackle the problem of data scarcity, the image pairs of cracked concrete and corresponding ground truth can be synthesized. In this work we propose a novel approach to stochastically model crack structures via Voronoi diagrams. The method is based on minimum-weight surfaces, an extension of shortest paths to 3d. Within a dedicated image processing pipeline, the surfaces are then discretized and embedded into real μCT images of concrete. The method is flexible and fast, such that a variety of different crack structures can be generated in a short amount of time.
Funder
Bundesministerium für Bildung und Forschung
Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau
Publisher
Springer Science and Business Media LLC
Reference37 articles.
1. Proc. Conf. Fractal;PS Addison,2000
2. Amhaz R, Chambon S, Idier J, Baltazart V. Automatic crack detection on 2D pavement images: an algorithm based on minimal path selection. IEEE Trans Intell Transp Syst. 2016;17:2718–29. https://doi.org/10.1109/TITS.2015.2477675.
3. Bargieł M, Mościński J. C-language program for the irregular close packing of hard spheres. Comput Phys Commun. 1991;64(1):183–92. https://doi.org/10.1016/0010-4655(91)90060-X.
4. Barisin T, Jung C, Müsebeck F, Redenbach C, Schladitz K. Methods for segmenting cracks in 3d images of concrete: a comparison based on semi-synthetic images. Pattern Recognit. 2022;129:108747. https://doi.org/10.1016/j.patcog.2022.108747.
5. Barisin T, Schladitz K, Redenbach C. Riesz networks: scale invariant neural networks in a single forward pass. https://arxiv.org/abs/2305.04665 (2023).
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献