Segmentation of cracks in 3d images of fiber reinforced concrete using deep learning

2025-01-30

AI Summary

Accurate 3D crack segmentation in fiber-reinforced concrete (FRC) CT images is hindered by fiber-induced interference, scarcity of pixel-level ground truth annotations, and poor cross-domain generalization. Method: We propose a microstructure-aware 3D U-Net variant, introducing a novel hybrid training paradigm that synergistically integrates semi-synthetic and real CT data. To our knowledge, this is the first adaptation of 3D U-Net for multi-scale crack segmentation under realistic fiber occlusion, incorporating transfer learning and multi-scale feature enhancement. Results: Evaluated on a real FRC CT dataset, our method achieves a Dice coefficient of 92.3%, substantially outperforming conventional thresholding and 2D segmentation approaches. It enables quantitative, micron-resolution 3D characterization of crack morphology—overcoming limitations of surface-only inspection—and provides interpretable, internal microstructural evidence to guide concrete mix design optimization and mechanical performance assessment.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• 3D Tomography
• Automated Crack Detection
• Fiber Reinforced Concrete

Innovation

• 3D Convolutional Neural Network
• Concrete Crack Detection
• Fiber Reinforced Concrete