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A Surface Crack Damage Evaluation Method Based on Kernel Density Estimation for UAV Images

Author

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  • Yusheng Liang

    (School of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
    These authors contributed equally to this work.)

  • Fan Zhang

    (School of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
    These authors contributed equally to this work.)

  • Kun Yang

    (School of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China)

  • Zhenqi Hu

    (School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

The development of UAV (unmanned aerial vehicle) technology provides an ideal data source for the information extraction of surface cracks, which can be used for efficient, fast, and easy access to surface damage in mining areas. Understanding how to effectively assess the degree of development of surface cracks is a prerequisite for the reasonable development of crack management measures. However, there are still no studies that have carried out a reasonable assessment of the damage level of cracks. Given this, this article proposes a surface crack damage evaluation method based on kernel density estimation for UAV images. Firstly, the surface crack information from the UAV images is quickly and efficiently obtained based on a machine learning method, and the kernel density estimation method is used to calculate the crack density. The crack nuclear density is then used as a grading index to classify the damage degree of the study area into three levels: light damage, moderate damage, and severe damage. It is found that the proposed method can effectively extract the surface crack information in the study area with an accuracy of 0.89. The estimated bandwidth of the crack kernel density was determined to be 3 m based on existing studies on the effects of surface cracks on soil physicochemical properties and vegetation. The maximum crack density value in the study area was 316.956. The surface damage area due to cracks was 14376.75 m 2 . The damage grading criteria for surface cracks in the study area (light: 0–60; moderate: 60–150; severe: >150) were determined based on the samples selected from the field survey by crack management experts. The percentages of light, moderate, and severe damage areas were 72.77%, 23.22%, and 4.01%, respectively. The method proposed in this article can effectively realize the graded damage evaluation of surface cracks and provide effective data support for the management of surface cracks in mining areas.

Suggested Citation

  • Yusheng Liang & Fan Zhang & Kun Yang & Zhenqi Hu, 2022. "A Surface Crack Damage Evaluation Method Based on Kernel Density Estimation for UAV Images," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16238-:d:994197
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    References listed on IDEAS

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    1. Yaokun Fu & Jianxuan Shang & Zhenqi Hu & Pengyu Li & Kun Yang & Chao Chen & Jiaxin Guo & Dongzhu Yuan, 2021. "Ground Fracture Development and Surface Fracture Evolution in N00 Method Shallowly Buried Thick Coal Seam Mining in an Arid Windy and Sandy Area: A Case Study of the Ningtiaota Mine (China)," Energies, MDPI, vol. 14(22), pages 1-18, November.
    2. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
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    Cited by:

    1. Fan Zhang & Zhenqi Hu & Yusheng Liang & Quanzhi Li, 2023. "Evaluation of Surface Crack Development and Soil Damage Based on UAV Images of Coal Mining Areas," Land, MDPI, vol. 12(4), pages 1-18, March.

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