Analysis of Machine Learning for Detect Concrete Crack Depths Using Infrared Thermography Technique

Arum Jang, Jihyung Kim, Min Jae Park, Young K. Ju, Sung Jig Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, much research with high-tech technology is being conducted in building inspection. In previous studies, thermography technology quickly and accurately inspected the concrete crack defects, and several machine learning models can reliably predict the crack depths. In this study, the most proper model would be proposed according to the concrete crack by evaluating the adaptability of the seven machine learning models. The models also predicted the crack depths, and the data were applied to each machine learning considering concrete temperature and external parameters. In machine learning, less critical features were ignored by filtering existing data to find useful features related to crack depths. Machine learning models are evaluated, and the structures of the models were investigated to determine the feature importance and part dependence. Those enabled us to decide the most proper machine learning according to the cracks.

Original languageEnglish
Title of host publicationIABSE Symposium Prague, 2022
Subtitle of host publicationChallenges for Existing and Oncoming Structures - Report
PublisherInternational Association for Bridge and Structural Engineering (IABSE)
Pages758-765
Number of pages8
ISBN (Electronic)9783857481833
Publication statusPublished - 2022
EventIABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures - Prague, Czech Republic
Duration: 2022 May 252022 May 27

Publication series

NameIABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report

Conference

ConferenceIABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures
Country/TerritoryCzech Republic
CityPrague
Period22/5/2522/5/27

Keywords

  • Building inspection
  • Crack depth prediction
  • Infrared thermography technique
  • Machine learning
  • Thermal camera

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction

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