In situ crack mapping of large-scale self-sensing concrete pavements using electrical resistance tomography

Sumit Gupta, Yun An Lin, Han Joo Lee, Jeff Buscheck, Rongzong Wu, Jerome P. Lynch, Navneet Garg, Kenneth J. Loh

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

This study aims to validate the large-scale application of self-sensing concrete in airport runway pavements and to use electrical resistance tomography (ERT) for characterizing spatially distributed damage during accelerated pavement testing. This self-sensing concrete not only retains the expected mechanical properties of typical airport pavements, but it can also sense deformation and strain. First, sensing properties were encoded in concrete pavements by modifying the cement-aggregate interface with multi-walled carbon nanotube (MWCNT) thin films. MWCNT thin films were spray-coated onto dried fine and coarse aggregates, and the film-coated aggregates were directly used for concrete casting. Second, an ERT algorithm was implemented for spatial conductivity mapping of self-sensing concrete pavements. Extensive laboratory tests were conducted on different sized specimens for characterizing their spatial damage detection performance. Last, a full-scale concrete airport pavement slab was cast with self-sensing concrete patches at locations where damage was expected. A heavy vehicle simulator was employed for accelerated pavement testing to induce cracks, while ERT measurements were collected at periodic intervals during testing. The results confirmed that the severities, locations, and patterns of cracks could be identified from the reconstructed ERT conductivity maps. Furthermore, subsurface damage features were identified prior to these cracks propagated and became visible on the surface.

Original languageEnglish
Article number104154
JournalCement and Concrete Composites
Volume122
DOIs
StatePublished - Sep 2021
Externally publishedYes

Funding

This research was supported by the U.S. Federal Aviation Administration (FAA) under Cooperative Agreement 13-G-017. Partial support was also provided by the Jacobs School of Engineering, University of California San Diego. Ms. Yun-An Lin was partially supported by the J. Yang Foundation Scholarship. The authors also thank Prof. John Harvey, Julio Paniagua, and Fabian Paniagua (UC Davis), Danlin Jiang (UC San Diego), Chunxiao Ning and Yinsheng Li (Dalian Institute of Technology), and Omid Bahrami (University of Michigan, Ann Arbor) for their assistance throughout various phases of this project. This research was supported by the U.S. Federal Aviation Administration (FAA) under Cooperative Agreement 13-G-017 . Partial support was also provided by the Jacobs School of Engineering, University of California San Diego . Ms. Yun-An Lin was partially supported by the J. Yang Foundation Scholarship. The authors also thank Prof. John Harvey, Julio Paniagua, and Fabian Paniagua (UC Davis), Danlin Jiang (UC San Diego), Chunxiao Ning and Yinsheng Li (Dalian Institute of Technology), and Omid Bahrami (University of Michigan, Ann Arbor) for their assistance throughout various phases of this project.

Keywords

  • Accelerated pavement testing
  • Carbon nanotube
  • Concrete
  • Conductivity
  • Cracks
  • Damage detection
  • EIT
  • ERT
  • Nanocomposite
  • Structural health monitoring

Fingerprint

Dive into the research topics of 'In situ crack mapping of large-scale self-sensing concrete pavements using electrical resistance tomography'. Together they form a unique fingerprint.

Cite this