Identifying spatial and temporal variations in concrete bridges with ground penetrating radar attributes

Vivek Kumar, Isabel M. Morris, Santiago A. Lopez, Branko Glisic

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Estimating variations in material properties over space and time is essential for the purposes of structural health monitoring (SHM), mandated inspection, and insurance of civil infrastructure. Properties such as compressive strength evolve over time and are reflective of the overall condition of the aging infrastructure. Concrete structures pose an additional challenge due to the inherent spatial variability of material properties over large length scales. In recent years, nondestructive approaches such as rebound hammer and ultrasonic velocity have been used to determine the in situ material properties of concrete with a focus on the compressive strength. However, these methods require personnel expertise, careful data collection, and high investment. This paper presents a novel approach using ground penetrating radar (GPR) to estimate the variability of in situ material properties over time and space for assessment of concrete bridges. The results show that attributes (or features) of the GPR data such as raw average amplitudes can be used to identify differences in compressive strength across the deck of a concrete bridge. Attributes such as instantaneous amplitudes and intensity of reflected waves are useful in predicting the material properties such as compressive strength, porosity, and density. For compressive strength, one alternative approach of the Maturity Index (MI) was used to estimate the present values and compare with GPR estimated values. The results show that GPR attributes could be successfully used for identifying spatial and temporal variation of concrete properties. Finally, discussions are presented regarding their suitability and limitations for field applications.

Original languageEnglish (US)
Article number1846
JournalRemote Sensing
Issue number9
StatePublished - May 1 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Earth and Planetary Sciences


  • Attribute analysis
  • Ground penetrating radar
  • In situ material property
  • Machine learning
  • Maturity method
  • Structural health monitoring


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