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
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Journal:
Drones

ISSN: 2504-446X

Year of publication: 2023

Volume: 7

Issue: 3

Pages: 149

Type: Article

DOI: 10.3390/DRONES7030149 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Drones

Abstract

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.

Funding information

Funders

Bibliographic References

  • Ali, (2022), Autom. Constr., 133, pp. 103989, 10.1016/j.autcon.2021.103989
  • Yao, (2014), Struct. Control Health Monit., 21, pp. 1387, 10.1002/stc.1655
  • Sa, P.Y. (2018). PAKYOUNGSA. [7th ed.].
  • (2022). Investigation of Defects in Apartment Houses, Calculation of Repair Costs and Standards for Determining Defects (Standard No. No.2021-1262). Available online: https://www.law.go.kr/행정규칙/공동주택하자의조사,보수비용산정및하자판정기준/.
  • Concrete Standard Specification (2022). Ministry of Land, Infrastructure and Transport, MOLIT Specification. Available online: https://www.law.go.kr/LSW/admRulLsInfoP.do?admRulSeq=2100000214235.
  • (2023, January 16). Ministry of Land, Infrastructure and Transport, MOLIT Statistics System. Available online: http://stat.molit.go.kr/portal/cate/engStatListPopup.do.
  • Park, (2021), Mag. RCR, 16, pp. 56
  • Lee, (2020), LHI J. Land Hous. Urban Aff., 11, pp. 83
  • Woo, H., Seo, D., Kim, M., Park, M., Hong, W., and Baek, S. (2022). Localization of Cracks in Concrete Structures using an Unmanned Aerial Vehicle. Sensors, 22.
  • Wang, (2017), IEEE Trans. Ind. Electron., 64, pp. 7293, 10.1109/TIE.2017.2682037
  • Salaan, (2018), J. Field Robot., 35, pp. 850, 10.1002/rob.21781
  • Akbar, (2019), Struct. Control Health Monit., 26, pp. e2276, 10.1002/stc.2276
  • Kim, I., Jeon, H., Baek, S., Hong, W., and Jung, H. (2018). Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection using an Unmanned Aerial Vehicle. Sensors, 18.
  • Oh, S., Ham, S., and Lee, S. (2021). Drone-Assisted Image Processing Scheme using Frame-Based Location Identification for Crack and Energy Loss Detection in Building Envelopes. Energies, 14.
  • Munawar, H.S., Ullah, F., Heravi, A., Thaheem, M.J., and Maqsoom, A. (2021). Inspecting Buildings using Drones and Computer Vision: A Machine Learning Approach to Detect Cracks and Damages. Drones, 6.
  • Wu, (2020), Urban Water J., 17, pp. 682, 10.1080/1573062X.2020.1758166
  • Tang, (2022), Structures, 37, pp. 426, 10.1016/j.istruc.2021.12.055
  • Que, (2023), Eng. Struct., 277, pp. 115406, 10.1016/j.engstruct.2022.115406
  • Seo, D.-M., Woo, H.-J., Kim, M.-S., Hong, W.-H., Kim, I.-H., and Baek, S.-C. (2022). Identification of Asbestos Slates in Buildings Based on Faster Region-Based Convolutional Neural Network (Faster R-CNN) and Drone-Based Aerial Imagery. Drones, 6.
  • (2021). Regulations on Structural Standards, etc. of Buildings (Standard No. No. 919). Available online: https://www.law.go.kr/법령/건축물의구조기준등에관한규칙.
  • Jung, (2019), J. Archit. Inst. Korea Struct. Constr., 35, pp. 163
  • Jeong, (2019), J. Archit. Inst. Korea Struct. Constr., 35, pp. 11
  • Liu, (2020), Comput.-Aided Civ. Infrastruct. Eng., 35, pp. 511, 10.1111/mice.12501
  • Kim, B., and Cho, S. (2018). Automated Vision-Based Detection of Cracks on Concrete Surfaces using a Deep Learning Technique. Sensors, 18.
  • Bang, (2017), Autom. Constr., 84, pp. 70, 10.1016/j.autcon.2017.08.031
  • Ayele, Y.Z., Aliyari, M., Griffiths, D., and Droguett, E.L. (2020). Automatic Crack Segmentation for UAV-Assisted Bridge Inspection. Energies, 13.
  • Zhu, (2022), Autom. Constr., 133, pp. 103991, 10.1016/j.autcon.2021.103991
  • Jiang, (2021), J. Perform. Constr. Facil., 35, pp. 04021092, 10.1061/(ASCE)CF.1943-5509.0001652
  • Maguire, (2018), Data Brief., 21, pp. 1664
  • Yaseen, (2018), IEEE Trans. Syst. Man Cybern. Syst., 49, pp. 253, 10.1109/TSMC.2018.2840341
  • Fang, (2021), BioResources, 16, pp. 5390, 10.15376/biores.16.3.5390-5406
  • Liao, Y., Mohammadi, M.E., and Wood, R.L. (2020). Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment. Drones, 4.
  • Zeybek, (2019), Measurement, 133, pp. 99, 10.1016/j.measurement.2018.10.013
  • Kim, I., Yoon, S., Lee, J.H., Jung, S., Cho, S., and Jung, H. (2022). A Comparative Study of Bridge Inspection and Condition Assessment between Manpower and a UAS. Drones, 6.
  • Baek, S., and Hong, W. (September, January 28). A Study on the Construction of a Background Model for Structure Appearance Examination Chart using UAV. Proceedings of the 2017 World Congress on Advances in Structural Engineering and Mechanics (ASEM), Ilsan, Republic of Korea.