README.md

metadata

license: mit
language:
  - en
pretty_name: Crack3D-Defect
size_categories:
  - 1B<n<10B

Crack3D-Defect

Crack3D-Defect is a multimodal 3D point cloud dataset designed for anomaly detection in infrastructure components such as masonry arches and tunnel rings. It includes synthetic data generated from FEM simulations and real scans of infrastructure collected at multiple time steps.

The code can be found at my GitHub repo.

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📁 Dataset Structure

infra_3DALv1/
│
├── syn_arch_src/
│   ├── memory_bank/
│   │   ├── min_cw0/           # Undeformed synthetic arches
│   │   └── min_cw0.4/         # X-displacement with 4mm minimum crack width
│   ├── disp_x/
│   │   ├── diff_cw/           # 4mm, 8mm crack width
│   │   └── diff_disps/        # Displacements: 8cm, 12cm, 16cm, 40cm
│   ├── disp_z/                # z-axis displacement
│   ├── disp_xz/               # x + z displacement
│   └── rot_x/                 # x-axis rotation
│
├── real_arch_src/
│   ├── memory_bank/           # London Bridge Station scans (2013/03/05)
│   ├── Narch_131123/          # Arch 1 after cracking (2013/11/23)
│   └── Sarch_131123/          # Arch 2 after cracking (2013/11/23)
│
└── tunnel/
    ├── memory_bank/           # Initial tunnel scan at loading step 0
    ├── 0-76-2.txt
    ├── 0-89-2.txt
    ├── 0-96-2.txt
    └── 0-103-2.txt            # Tunnel scans under increasing load

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🔹 File Formats

Folder	Format	Description
syn_arch_src/	.csv	Columns: categoryID (0–3), [X, Y, Z], [X_noise, Y_noise, Z_noise], intensity
real_arch_src/	.asc	Columns: [x, y, z, r, g, b, intensity]
tunnel/	.txt	Columns: [x, y, z, intensity]

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🔗 Labels

• categoryID for synthetic data (syn_arch_src/):
  • 0: No crack
  • 1: Intrados crack
  • 2: Extrados crack
  • 3: Inner crack

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📌 Citation

If you use this dataset, please cite the associated PhD thesis and publications.

@article{jing2024anomaly,
  title={Anomaly detection of cracks in synthetic masonry arch bridge point clouds using fast point feature histograms and PatchCore},
  author={Jing, Yixiong and Zhong, Jia-Xing and Sheil, Brian and Acikgoz, Sinan},
  journal={Automation in Construction},
  volume={168},
  pages={105766},
  year={2024},
  publisher={Elsevier}
}

@article{jing20253d,
  title={3D multimodal feature for infrastructure anomaly detection},
  author={Jing, Yixiong and Lin, Wei and Sheil, Brian and Acikgoz, Sinan},
  journal={Automation in Construction},
  volume={178},
  pages={106388},
  year={2025},
  publisher={Elsevier}
}

@phdthesis{jing2025development,
  title={Development of synthetic point cloud data and deep learning algorithms for automatic segmentation and defect detection in masonry bridges},
  author={Jing, Yixiong},
  year={2025},
  school={University of Oxford}
}

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License

Our work is subjected to MIT License.