Defining Structural Cracks in Exterior Walls of Concrete Buildings Using an Unmanned Aerial Vehicle

  1. Woo, Hyun-Jung 1
  2. Hong, Won-Hwa 1
  3. Oh, Jintak 2
  4. Baek, Seung-Chan 2
  5. Verhoeven, Geert
  6. González Aguilera, Diego 3
  1. 1 School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
  2. 2 Department of Architecture, Kyungil University, Gyeongsan 38428, Republic of Korea
  3. 3 Universidad de Salamanca
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    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

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Revista:
Drones

ISSN: 2504-446X

Año de publicación: 2023

Volumen: 7

Número: 3

Páginas: 149

Tipo: Artículo

DOI: 10.3390/DRONES7030149 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Drones

Objetivos de desarrollo sostenible

Resumen

n Republic of Korea, cracks in concrete structures are considered to be objective structural defects, and the constant maintenance of deteriorating facilities leads to substantial social costs. Thus, it is important to develop technologies that enable economical and efficient building safety inspection. Recently, the application of UAVs and deep learning is attracting attention for efficient safety inspection. However, the currently developed technology has limitations in defining structural cracks that can seriously affect the stability of buildings. This study proposes a method to define structural cracks on the outer wall of a concrete building by merging the orthoimage layer and the structural drawing layer with the UAV and deep learning that were previously applied during a safety inspection. First, we acquired data from UAV-based aerial photography and detected cracks through deep learning. Structural and non-structural cracks were defined using detected crack layer, design drawing layer defined the structural part, and the orthoimage layer was based on UAV images. According to the analysis results, 116 structural parts cracks and 149 non-structural parts cracks were defined out of a total of 265 cracks. In the future, the proposed method is expected to greatly contribute to safety inspections by being able to determine the quality and risk of cracks.

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