An advanced analytical study on linseed oil paint binder

Drying oils, and in particular linseed oil, were the most common binding media employed in painting between XVI and XIX centuries. Artists usually operated some pre-treatments on the oils to obtain binders with modified properties, such as different handling qualities or colour. Oil processing has a key role on the subsequent ageing of and degradation of linseed oil paints. In this thesis a multi-analytical approach was adopted to investigate the drying, polymerization and oxidative degradation of the linseed oil paints. In particular, thermogravimetry analysis (TGA), yielding information on the macromolecular scale, were compared with gas-chromatography mass-spectrometry (GC-MS) and direct exposure mass spectrometry (DEMS) providing information on the molecular scale. The study was performed on linseed oils and paint reconstructions prepared according to an accurate historical description of the painting techniques of the 19th century. TGA revealed that during ageing the molecular weight of the oils changes and that higher molecular weight fractions formed. TGA proved to be an excellent tool to compare the oils and paint reconstructions. This technique is able to highlight the different physical behaviour of oils that were processed using different methods and of paint layers on the basis of the different processed oil and /or the pigment used. GC/MS and DE-MS were used to characterise the soluble and non-polymeric fraction of the oils and paint reconstructions. GC/MS allowed us to calculate the ratios of palmitic to stearic acid (P/S), and azelaic to palmitic acid (A/P) and to evaluate effects produced by oil pre-treatments and the presence of different pigments. This helps to understand the role of the pre-treatments and of the pigments on the oxidative degradation undergone by siccative oils during ageing. DE-MS enabled the various molecular weight fractions of the samples to be simultaneously studied, and thus helped to highlight the presence of oxidation and hydrolysis reactions, and the formation of carboxylates that occur during ageing and with the changing of the oil pre-treatments and the pigments. The combination of thermal analysis with molecular techniques such as GC-MS, DEMS and FTIR enabled a model to be developed, for unravelling some crucial issues: 1) how oil pre-treatments produce binders with different physical-chemical qualities, and how this can influence the ageing of an oil paint film; 2) which is the role of the interaction between oil and pigments in the ageing and degradation process.

Exploiting the environment for structural health monitoring: Strategies, algorithms, and technologies

A densely built environment is a complex system of infrastructure, nature, and people closely interconnected and interacting. Vehicles, public transport, weather action, and sports activities constitute a manifold set of excitation and degradation sources for civil structures. In this context, operators should consider different factors in a holistic approach for assessing the structural health state. Vibration-based structural health monitoring (SHM) has demonstrated great potential as a decision-supporting tool to schedule maintenance interventions. However, most excitation sources are considered an issue for practical SHM applications since traditional methods are typically based on strict assumptions on input stationarity. Last-generation low-cost sensors present limitations related to a modest sensitivity and high noise floor compared to traditional instrumentation. If these devices are used for SHM in urban scenarios, short vibration recordings collected during high-intensity events and vehicle passage may be the only available datasets with a sufficient signal-to-noise ratio. While researchers have spent efforts to mitigate the effects of short-term phenomena in vibration-based SHM, the ultimate goal of this thesis is to exploit them and obtain valuable information on the structural health state. First, this thesis proposes strategies and algorithms for smart sensors operating individually or in a distributed computing framework to identify damage-sensitive features based on instantaneous modal parameters and influence lines. Ordinary traffic and people activities become essential sources of excitation, while human-powered vehicles, instrumented with smartphones, take the role of roving sensors in crowdsourced monitoring strategies. The technical and computational apparatus is optimized using in-memory computing technologies. Moreover, identifying additional local features can be particularly useful to support the damage assessment of complex structures. Thereby, smart coatings are studied to enable the self-sensing properties of ordinary structural elements. In this context, a machine-learning-aided tomography method is proposed to interpret the data provided by a nanocomposite paint interrogated electrically.