Natural stones used in historical monuments are open porous systems that are subjected to different deterioration processes because of their exposure to the environment. Generally, one can identify three types of degradation:

• Physical: induced by physical-mechanical processes, such as salt crystallization, erosion, thermal expansion, etc. Such phenomena generate structural solicitations strongly depending on mineralogical composition, grain and stone texture;

• Chemical: induced by chemical processes such as carbonation, sulphation, oxidation, hydration, determining modifications of the composition of the material.

• Biological: caused by the presence of micro-organisms, animals and humans.

In order to reduce the effects of these deteriorating factors, strategies for stone protection have been developed. Due to the uniqueness of each Cultural Heritage (CH) work of art, the design of an effective treatment for a given lithotype should be tuned in order to meet its specific requirements, related to the water uptake of the monitored porous medium.

Recently predictive maintenance has emerged as a new tool for monitoring and protecting CH sites. Predictive maintenance consists in anticipating future failure of some asset through appropriate diagnostic techniques. 

The main goal of the project is to develop a prototype of Digital-Twin (DT) of CH monitored sites based on a mathematical simulation algorithm for porous materials taking into account multiple degradation factors based on laboratory experiments on materials and considering environmental data. As a result, thanks to a better understanding of the underlying phenomena, as well as the qualitative and quantitative description of the emergent dynamics for the identification of the cause/effect mechanisms responsible for chemical and structural deterioration of CH stones, DT’s outputs will allow the optimization of conservation strategies and of the application of protective treatments. Our aim here is to use a model-based approach to predictive maintenance, where the degradation processes of CH are described by PDE-based mathematical models, that will be calibrated by means of experimental data with the application of optimization tools.