Semi-quantitative assessment system of expected moisture levels in compact roofs Available to Purchase

The zero-emission goal and the climate change challenge in the building sector have boosted the interest on roof constructions. As roofs are increasingly used for energy generation via photovoltaic panels and for buffering heavy rain events, the risk of leakages due to building defects is also growing. System and technology development with focus on leakages’ risk assessment through prediction, warning, and damage limitation is therefore essential. To this purpose, the moisture development in the roof represents a key aspect to evaluate. Due to seasonal variations, the moisture level is highest in the outer parts of the roof during the winter months, when condensation might occur; therefore, this might not be a sign of water leakage, but a consequence of normal transiently elevated moisture levels. Sensor technologies to be used in roofs for monitoring moisture conditions are in constant development. However, there is a lack of integrated solutions that are able to distinguish between moisture caused by rain leakages or air leakage from the indoor, and moisture due to the natural redistribution in the roof construction. In this article, typical compact roofs and climatic conditions in Norway were identified. Then, one-dimensional heat and moisture transfer simulations were performed with WUFI Pro software. The results of these simulations were used to develop a semi-quantitative system for assessing the moisture conditions in compact roofs. This system is characterized by tables with the description of expected temperature and relative humidity levels in different roof configurations under various indoor and outdoor environmental conditions. A grading system was also defined to evaluate whether the identified moisture situation is most likely safe, less safe, or unsafe. The assessment system described in this article is meant to be linked to sensor technologies for leakage warning in compact roofs, aiming to reduce false alarms by identifying expected abnormal levels.