There are several techniques for the performance of energy studies in buildings, where infrared thermography is widely used for the study of the composition of the envelopes, to find faults in the building materials and in the composition of the envelopes with influence on their thermal behaviour, as well as to detect areas with humidity. Common thermographic building interpretations are performed by a human operator, which involves a high level of subjectivity and mainly relies on the expertise of the operator. With the aim at minimizing this subjectivity and maximizing the accuracy of the inspections, this paper presents a procedure for the automation of thermographic building inspections mainly focused on thermal bridges. The procedure, in addition to detecting the thermal bridges by their geometric characteristics and their temperature differences with the surroundings, includes the computation of the thermophysical property of linear thermal transmittance of each candidate to thermal bridge, thus implying their characterization in addition to their detection. With this addition, together with a previous process of rectification of the thermal images analysed, the accuracy of the detection of thermal bridges regarding existing methodologies is improved in 15% considering the false positives and negatives obtained in each methodology.
Moisture is a pathology that damages all type of construction materials, from materials of building envelopes to materials of bridges. Its presence can negatively affect the users' conditions of indoor comfort. Furthermore, heating and cooling energy demand can be increased by the presence of moist materials. Infrared thermography (IRT) is a common technique in the scientific field to detect moisture areas, because of its non-destructive, non-contact nature. In addition, IRT allows an earlier moisture detection compared to the analysis using visible images. In order to optimize thermographic inspections, this paper presents one of the first methodologies for the automatic detection of moisture areas affecting the surface of construction materials. The methodology is based on the application of visible image processing techniques adapted to thermographic images through the consideration of an image conversion format, a thermal criterion and a thermal and a geometric filter. The precision, recall and F-score parameters obtained are around 83.5%, 73.5% and 72.5%, respectively, considering the false positives/negatives through a series of 12 tests made in different construction materials and ambient conditions, comparing the preliminary results with existing methodologies. Keywords Moisture Á Infrared thermography Á Automation Á Adapted visible image processing Á Construction materials Á Different environmental conditions & I. Garrido
Health monitoring and prediction in different types of structures is essential in order to maintain optimal conditions. Some of the pathologies that affect their structural stability are characterized by distinct thermal properties compared to unaltered areas. Infrared thermography (IRT) is a technique based on the acquisition of the thermal radiation of the bodies using thermal sensors of infrared (IR) cameras, which produce an image of the thermal infrared radiation captured through the conversion of the radiation values to temperature values. Therefore, this technique can be used in different studies to analyse structures with one or more pathologies based on their anomalous thermal behaviour with regard to the unaltered surroundings. As a consequence, this review presents various IRT applications to infrastructure inspections, showing the utility of the technique.
Infrared thermography (IRT) is widely used for the study of the composition of building envelopes and the detection of faults in the building materials with influence on their thermal behaviour such as thermal bridges and moistures. The automation of the interpretation of the thermal images is proposed as a solution to help in the identification of the pathologies, speeding up the inspection process as well as avoiding the loss of detection of existing pathologies, or their misinterpretation. Specifically, thermal bridges and moisture areas are detected and classified, and their contours are extracted through the temperature analysis of the thermal images.
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