Environmental, climatic, and atmospheric factors threaten the integrity of stone monuments, causing different forms and degrees of deterioration (stone weathering), both in the internal structure of the building stones and on the surface. Due to the complexity of this process, the accurate definition of the origins, types, levels of weathering and their classification is needed, to prevent erroneous intervention in conservation practices. Different classifications have been proposed during the years, traditionally starting from visual examination and then applying more quantitative approaches, expecially based on NDT.
Determining the weathering classification of stone cultural heritage via the analytic hierarchy process and fuzzy inference system,
by Mehmet Ergün Hatır, published on the Journal of Cultural Heritage, Volume 44, 2020, Pages 120-134,
makes a step forward,
defining an integrated weathering classification (IWC) to identify the levels of weathering, by combining two different weathering classifications, produced with visual analysis and P-wave velocity data.
First, a new visual weathering classification (VWC) model, based on the analytical hierarchy process (AHP) and presenting objective approaches with the views of 30 experts from different disciplines, was developed. The AHP method is a multi-decision method that selects between alternatives beneath the certainty and uncertainty of the decision maker: complex problems can be solved precisely by the AHP method, minimizing the subjectivity of the evaluation. The VWI (visual weathering index) value for each building stone was calculated according to the type and level of weathering identified in the monument.
Then, a P-wave velocity classification (VpWC) was created with the P-wave velocity method, a non destructive technique commonly used to detect physical and mechanical changes in building stones due to weathering.
Maps that included VWI and VpWI values from each building block of the Ateş Baz-ı Veli Mausoleum, in the city of Konya, were created. However, these maps exhibited some discrepancies between the classifications. To eliminate the uncertainties between these two classifications, an integrated weathering classification (IWC) was created by using the Mamdani algorithm, a fuzzy inference system which facilitates collective evaluation; in fuzzy logic, limitations are not precise, and complex and multivariate problems can be easily solved because the degree to which an element belongs to the set varies between zero and one.
The model from the Mamdani algorithm in this study comprises two different inputs (VWC and VpWC) and one output (IWC). Five fuzzy sets were defined for each of the input and output variables: very slight (VS), slight (S), moderate (M), high (H), and very high (VH).
The IWC method presents realistic estimations when identifying weathering levels in building stones because the data from the VWC and VpWC can be evaluated together. It offers a different approach compared to previous studies because it quantitatively classifies physical changes on the surfaces and/or subsurfaces of building stones. Thanks to the easy applicability and holistic approaches of the IWC, the errors that are encountered when determining the degrees of weathering for stone monuments can be minimized and it could provide a useful basis for conservation-restoration practices.