Summary Masonry heritage structures are often affected by slow irreversible deterioration mechanisms that can jeopardise structural stability in the foreseeable future. Static structural health monitoring (SHM), aimed at the continuous measurement of key slow‐varying parameters, has the potential to identify such mechanisms at a very early stage. This can greatly facilitate the implementation of adequate preventive and remedial measures, which can be critical to ensure that such structures are preserved for generations to come. However, because monitored parameters usually experience reversible seasonal variations of the same order of magnitude as changes caused by active mechanisms, identification of the latter is often a difficult task. This paper presents a fully integrated automated data analysis procedure for complete static SHM systems utilising dynamic linear regression models to filter out the effects caused by environmental variations. The method does not only produce estimated evolution rates but also classifies monitored responses in predefined evolution states. The procedure has successfully been used to identify vulnerable areas in two important medieval heritage structures in Spain, namely, the cathedral of Mallorca and the church of the monastery of Sant Cugat.

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Transparent conducting oxides (TCOs) used in solar cells must be optimized to achieve minimum parasitic absorption losses while providing sufficient lateral conductivity. Low contact resistance with the adjacent device layers...

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