Infrared thermography (IRT) is a technique increasingly used in building inspection. If in many applications it is sufficient to analyze the thermal patterns, others exist in which the exact determination of the surface temperature is a fundamental aspect. In these circumstances, the emissivity of the surfaces assumes special relevance, being probably the most important property in the definition of the boundary conditions. However, information on the uncertainty involved in its measurement, as well as the conditions that influence it, is scarce. This article presents an innovative contribution both to the characterization of the emissivity of various construction materials, and to the discussion of emissivity measurement procedures and the attendant uncertainty. In this sense, three experimental campaigns were carried out: T.I, preliminary tests to assess the initial conditions required for an accurate IRT measurement of the emissivity (reference tape and position of the camera); T.II, assessment of the emissivity of nine different building materials, in dry conditions, using the emissometer and the IRT and black tape methods; and T.III, assessment of the emissivity of three materials during the drying process. The results confirmed that emissivity is a crucial parameter for the accurate measurement of surface temperature. Emissivity measurements carried out with IRT (black tape method) and with the emissometer returned meaningful differences when compared with the values available in the literature. This disagreement led to surface temperature differences of up to 7 °C (emissometer versus reference values). This research also highlighted that the moisture content of the materials influences the emissivity values, with fluctuations that can be greater than 10%, and that the effect of moisture is visible even for low values of moisture content.

For scheduling track maintenance, infrastructure managers perform inspections to assess the track condition. When the irregularities are higher than the threshold limits, a track has a defect that should be corrected to avoid future failures or traffic disruption. Scheduling maintenance actions contribute to reliability and availability but demand the prediction of the evolution of track degradation. In recent years, several degradation models have been defined to forecast geometrical evolution over time and/or tonnage, mainly for heavy rail systems. Nevertheless, most of those models have limitations when dealing with measured data collected in different time intervals as happens in reality. To overcome this problem, a data-driven model based on the logistic binary function is presented and validated with real inspection measurements. The results prove that the model has a 91.1% success rate, an excellent discrimination ability, and a high sensitivity, classifying correctly 84.1% of inspections in need of maintenance. The model also has high specificity as it classifies 94.5% of inspections with no demand of maintenance action. The model is easy to implement, which is also an advantage for the track asset management with guaranty of excellent sensitivity and discrimination.