Investigating the Potential of Infrared Thermography to Inform on Physical and Mechanical Properties of Soils for Geotechnical Engineering

Knowledge of physical and mechanical properties of geomaterials is fundamental to characterise their response to external forcings (mechanical, climatic) at various scales. This is true, for instance, in slope stability assessments, civil engineering works, and agriculture. The direct evaluation of these properties in situ can be difficult, especially in inaccessible or vast areas, and so can be the sampling and subsequent testing in the laboratory—where ensuring the representativeness of the acquired data at the scale of analysis poses an additional challenge. Thus, empirical correlations with more readily determinable quantities remain a powerful and practical tool. Recently, several sensors, able to inform on various geomaterial properties, have been developed. However, applications have typically targeted rocks, while studies on uncemented geomaterials (soils, geotechnically speaking) are lacking. Here, we propose a simple method to evaluate the porosity and critical state friction angle of soils via infrared thermography, consisting of periodic acquisitions of images in infrared wavelengths. To demonstrate the method’s capability, we analysed the cooling behaviour of samples of bentonite, kaolin, and sand (for which an extensive characterisation exists in the literature), after compaction to different porosities and pre-heating in an oven. We interpreted the results by seeking the optimal time interval for which a cooling rate index (CRI) could be defined, which is best linked with the target property. We found that the CRI correlates very well with the critical state friction angle (R > 0.85) and that different materials show unique and strong (R = 0.86–0.99) relationships between their porosity and the CRI, which also varies in a material-specific fashion according to the explored time interval. Although a systematic investigation on a wide range of natural soils is warranted, we argue that our method can be highly informative and could be used to calibrate remote sensing-based full-scale implementations in situ for various purposes.