Machine learning and numerical simulations for predicting critical crack conditions in wooden panels

Purpose: Artworks made of hygroscopic materials, like wooden panel paintings, are susceptible to environmental conditions. Traditional panel paintings typically consist of a wooden panel coated with layers of gesso, paint and varnish. Due to environmental fluctuations, the gesso layer and the wood panel may respond differently to moisture changes, triggering potential fractures. The investigation of such phenomena is of high interest, but it is still scarcely studied by engineers. Design/methodology/approach: The proposed study aimed to create a simplified 3D finite element model for paintings to identify environmental conditions that could exceed critical strain levels. A penny-shaped crack within the gesso layer was modelled and, after applying a given deformation, the strain energy density failure criterion was used to assess if the crack was in a critical state. Findings: Various combinations of geometric parameters of the model were explored, and to save computational time and cost, machine learning algorithms (namely extreme gradient boosting machines and Gaussian process regression algorithms) were introduced. The analyses were carried out on different panel paintings 3D models obtained by varying the wooden species and the boundary conditions, for exploring a wide number of combinations. Originality/value: Moreover, the integration of machine learning can potentially reduce the reliance on numerical simulations and offer new insights into the conservation of artworks, a field in which such tools are still scarcely exploited.