Bridge deck delamination identification from unmanned aerial vehicle infrared imagery

A. Ellenberg, A. Kontsos, F. Moon, I. Bartoli

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

The rapid, cost-effective, and non-disruptive assessment of bridge deck condition has emerged as a critical challenge for bridge maintenance. Deck delaminations are a common form of deterioration which has been assessed, historically, through chain-drag techniques and more recently through nondestructive evaluation (NDE) including both acoustic and optical methods. Although NDE methods have proven to be capable to provide information related to the existence of delaminations in bridge decks, many of them are time-consuming, labor-intensive, expensive, while they further require significant disruptions to traffic. In this context, this article demonstrates the capability of unmanned aerial vehicles (UAVs) equipped with both color and infrared cameras to rapidly and effectively detect and estimate the size of regions where subsurface delaminations exist. To achieve this goal, a novel image post-processing algorithm was developed to use such multispectral imagery obtained by a UAV. To evaluate the capabilities of the presented approach, a bridge deck mockup with pre-manufactured defects was tested. The major advantages of the presented approach include its capability to rapidly identify locations where delaminations exist, as well as its potential to automate bridge-deck related damage detection procedures and further guide investigations using other higher accuracy and ground-based approaches.

Original languageEnglish (US)
Pages (from-to)155-165
Number of pages11
JournalAutomation in Construction
Volume72
DOIs
StatePublished - Dec 1 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

Fingerprint

Dive into the research topics of 'Bridge deck delamination identification from unmanned aerial vehicle infrared imagery'. Together they form a unique fingerprint.

Cite this