Understanding the mechanical behavior and properties of works of art, such as panel paintings, is helpful for evaluating the failure mechanisms in play, especially during exhibitions in confined spaces. A panel painting is typically formed by a wooden panel and by layers of gesso, followed by paints and varnishes on the top. Induced by environmental changes, the mismatch in the moisture response of a stiff gesso layer and of the underlying wood panel produces risk of fracture in the pictorial layer. This happens because the gesso layer experiences tension which leads to cracking if the mechanical strain exceeds a critical level. The proposed contribution aims to develop a 3D simplified model for paintings, to detect the environmental conditions which may lead to exceeding the critical strain levels. To this aim, a penny-shaped crack has been simulated inside the gesso layer, centered along the symmetrical planes. To establish if the crack is in critical conditions, the failure criterion of the strain energy density (SED) method has been used: when the critical SED value is reached, the crack is assumed to be in unsafe conditions, otherwise it is in safe conditions. Several combinations of the geometric parameters describing the model have been checked, allowing to define whether the different conditions are likely to lead to a further damage development. Finally, the obtained results have been used to train a preliminary extreme gradient boosting machine (XGBoost) that may be able to classify and predict the two possible outcomes: safe and unsafe. This way, by exploiting the capabilities of machine learning, it could be possible to limit the need of numerical simulations and to introduce new rationales in the framework of work of arts conservation

Predicting damage evolution in panel paintings with machine learning

Califano A.;Baiesi M.;
2022

Abstract

Understanding the mechanical behavior and properties of works of art, such as panel paintings, is helpful for evaluating the failure mechanisms in play, especially during exhibitions in confined spaces. A panel painting is typically formed by a wooden panel and by layers of gesso, followed by paints and varnishes on the top. Induced by environmental changes, the mismatch in the moisture response of a stiff gesso layer and of the underlying wood panel produces risk of fracture in the pictorial layer. This happens because the gesso layer experiences tension which leads to cracking if the mechanical strain exceeds a critical level. The proposed contribution aims to develop a 3D simplified model for paintings, to detect the environmental conditions which may lead to exceeding the critical strain levels. To this aim, a penny-shaped crack has been simulated inside the gesso layer, centered along the symmetrical planes. To establish if the crack is in critical conditions, the failure criterion of the strain energy density (SED) method has been used: when the critical SED value is reached, the crack is assumed to be in unsafe conditions, otherwise it is in safe conditions. Several combinations of the geometric parameters describing the model have been checked, allowing to define whether the different conditions are likely to lead to a further damage development. Finally, the obtained results have been used to train a preliminary extreme gradient boosting machine (XGBoost) that may be able to classify and predict the two possible outcomes: safe and unsafe. This way, by exploiting the capabilities of machine learning, it could be possible to limit the need of numerical simulations and to introduce new rationales in the framework of work of arts conservation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3454624
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