Time-lapse thermography for building defect detection

Matthew Fox, David Coley, Steve Goodhew, Pieter De Wilde

Research output: Contribution to journalArticlepeer-review

62 Citations (SciVal)
74 Downloads (Pure)


Building thermography traditionally captures the thermal condition of building fabric at one single point in time, rather than changes in state over a sustained period. Buildings, materials and the environment are, however, rarely in a thermal equilibrium, which therefore risks the misinterpretation of building defects by employing this standard methodology. This paper tests the premise that time-lapse thermography can better capture building defects and dynamic thermal behaviour. Results investigating the temporal resolution required for time-lapse thermography over two case study houses found that under typical conditions small temperature differences (approximately 0.2 K) between thermal areas could be expected for 30-min image intervals. Results also demonstrate that thermal patterns vary significantly from day-to-day, with a 2.0 K surface temperature difference experienced from one day to the next. Temporal resolutions needed adjusting for different types of construction. Time-lapse experiments raised practical limitations for the methodology that included problems with the distance to target and foreground obstructions. At the same time, these experiments show that time-lapse thermography could greatly improve our understanding of building transient behaviour and possible building defects. Time-lapse thermography also enables enhanced differentiation between environmental conditions (such as clear sky reflections), actual behaviour and construction defects, thereby mitigating the risk of misinterpretation.

Original languageEnglish
Pages (from-to)95-106
Number of pages12
JournalEnergy and Buildings
Early online date11 Feb 2014
Publication statusPublished - 1 Apr 2015


  • Defect detection
  • Time-lapse thermography
  • Transient behaviour


Dive into the research topics of 'Time-lapse thermography for building defect detection'. Together they form a unique fingerprint.

Cite this