Читать книгу Monument Future - Siegfried Siegesmund - Страница 384

With the aim to highlight a functional link between water content and surface temperature, a FLIR microbolometer (sc655, spectral range of 7.5–13 µm and ±2 °C (or ±2 % of reading) accuracy is used and vertically positioned in an apparatus to ensure the calibration of the focal length and the orientation of the camera. A parallelepiped sample is positioned near a perfect reflector on a table. The apparatus is at room temperature with a restriction of the light noise. The selected geometry of the sample is done to prevent against optic default.

Dimensions of the limestone samples are given as follow (38.9 ±0.4 mm width, 62.9 ±0.4 mm height and 10.5 ±0.6 mm thick).

The samples have been dried in a stove at 65 °C until mass became stable. Then partial saturation and homogenization of the sample have been done from ≈0 % to ≈100 %. The lowest saturation corresponds to the amount of absorbed water at environmental conditions while the maximum water content is represented by the samples completely saturated using the 48 h porosity protocol.

The partially saturated samples are then placed in a chamber with a relative humidity of 100 % for a duration of 15 days to allow the diffusion of the water within the volume of the sample to obtain a homogeneous distribution.

Illustration of the IR Thermography measurement is given in the figure 3. For each sample, several thermograms are registered in order to statistically determine its relevance. In the thermal scene, the perfect reflector (crumpled aluminum-foil) has a predictable emissivity that allows to estimate and filter the environmental noise affecting the measurements. Moreover, such a reference is used to 254calibrate the apparent temperature delivered by the infrared camera.

Figure 3: Thermogram of the sample and the crumpled aluminum-foil (reference) showing the different ROI tested.

The statistical relevance of the data is determined by the temperature analyses of each region of interest (ROI) of all the samples. Such analyses are done over ten images of the thermal scene. The statistical moments (mean, standard deviation, Skewness and Kurtosis) are calculated. The analysis of those parameters allows to determine the best ROI that will be used to define the calibration function representing the thermal response over the water content variation.