This project offers a methodology for the analysis of resistographic profiles obtained from samples of wood and the extraction of precise data from these profiles. Tests were conducted on healthy samples of wood from six different species, as well as on “old” wood in use as part of timber structures with an age of 80-120 years. Based on the data collected using the resistograph, a series of variables was determined, and statistically analysed. An analysis of the data reveals the close relation between the resistographic variables and wood density (R2> 90%). Equally, in the case of healthy wood, an analysis of the defined variables permits the identification of the species of the sample with a very high probability and, therefore, its indirect resistance values.

This paper presents the design, development and testing of a low-cost Structural Health Monitoring (SHM) system based on MEMS (Micro Electro-Mechanical Systems) triaxial accelerometers. A new control system composed by a myRIO platform, managed by specific LabVIEW software, has been developed. The LabVIEW software also computes the frequency response functions for the subsequent modal analysis. The proposed SHM system was validated by comparing the data measured by this set-up with a conventional SHM system based on piezoelectric accelerometers. After carrying out some validation tests, a high correlation can be appreciated in the behavior of both systems, being possible to conclude that the proposed system is sufficiently accurate and sensitive for operative purposes, apart from being significantly more affordable than the traditional one.

Infrared thermography (IRT) is a technique that allows the visualization of the surface temperature of objects from the radiation that they emit without any kind of contact. This non-destructive technique (NDT) is used worldwide for locating and identifying internal defects in materials from the thermal anomalies that these defects generate on the surface of the materials, among other applications. Two types of irregularities are found in wood: singularities and defects, which are regions of disorder that affect the quality of the wood and decrease its resistance capacity. Based on the local density variations caused by these irregularities that alter the thermodynamic behavior of the wood, the present investigation analyzed the capacity and performance of IRT for the exploration and detection of these subsurface singularities and defects in wood. The results establish the true scope of this technique in detecting subsurface defects in wood and verifying the complexity inherent in the analysis.