Machine Learning-Based Concrete Crack Depth Prediction Using Thermal Images Taken under Daylight Conditions

Min Jae Park, Jihyung Kim, Sanggi Jeong, Arum Jang, Jaehoon Bae, Young K. Ju

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Concrete cracks can threaten the usability of structures and degrade the aesthetics of buildings. Furthermore, minor cracks can develop into large-scale cracks that may lead to structural failure when exposed to excessive external loads. In addition, the concrete crack width and depth should be precisely measured to investigate the effects of concrete cracks on the stability of structures. Thus, a nondestructive and noncontact testing method was introduced for detecting concrete crack depth using thermal images and machine learning. The thermal images of the cracked specimens were obtained using a constant test setup for several months under daylight conditions, which provided sufficient heat for measuring the temperature distributions of the specimens, with recording parameters such as air temperature, humidity, and illuminance. From the thermal images, the crack and surface temperatures were obtained depending on the crack widths and depths using the parameters. Four machine-learning algorithms (decision tree, extremely randomized tree, gradient boosting, and AdaBoost) were selected, and the results of crack depth prediction were compared to identify the best algorithm. In addition, data bias analysis using principal component analysis, singular value decomposition, and independent component analysis were conducted to evaluate the efficiency of machine learning.

Original languageEnglish
Article number2151
JournalRemote Sensing
Issue number9
Publication statusPublished - 2022 May 1

Bibliographical note

Funding Information:
Funding: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2021R1A5A1032433 and NRF-2020R1A2C3005687). The authors are grateful to the authorities for their assistance.

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • crack detecting method
  • data bias analysis
  • machine learning
  • macrocrack
  • thermal images

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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