Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building materials - Archive ouverte HAL Access content directly
Journal Articles Energy and Buildings Year : 2011

Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building materials

(1) , (1) , (1) , (1) , (2)
1
2

Abstract

The actual European energy context highlights the building sector as one of the largest sectors of energy consumption. Consequently, the "Energy Performance of Buildings Directive", adopted in 2002 and focusing on energy use in buildings, requires all the EU members to enhance their building regulations and to introduce energy certification schemes, with the aim of both reducing energy consumption and improving energy efficiency. That is why carrying out an energy performance diagnosis is mandatory, notably when buying or selling properties. Indeed, invisible defaults, like, for example, non-emerging cracks or delaminations, could have a detrimental effect on insulating qualities. Esimaing in-situ thermo-physical properties allowing locating these defaults, the present work focuses on proposing new and efficient approaches based on the use of both artificial intelligence tools (artificial neural networks and neuro-fuzzy systems) and inverse methods for characterizing building materials i.e. for estimating their thermal diffusivity using thermograms obtained thanks to a non-destructive photothermal method.
Fichier principal
Vignette du fichier
Grieu2011.pdf (996.93 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01273275 , version 1 (12-02-2016)

Licence

Attribution - NonCommercial - NoDerivatives - CC BY 4.0

Identifiers

Cite

Stéphane Grieu, Olivier Faugeroux, Adama Traoré, Bernard Claudet, Jean-Luc Bodnar. Artificial intelligence tools and inverse methods for estimating the thermal diffusivity of building materials. Energy and Buildings, 2011, 43 (2-3), pp.543-554. ⟨10.1016/j.enbuild.2010.10.020⟩. ⟨hal-01273275⟩
149 View
302 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More